Transcutaneous analyte sensor

ABSTRACT

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for transcutaneous measurement of glucose in a host.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No. 11/334,876 filed Jan. 18, 2006 now U.S. Pat. No. 7,774,145. U.S. application Ser. No. 11/334,876 is a continuation-in-part of U.S. application Ser. No. 10/633,367 filed Aug. 1, 2003 now U.S. Pat. No. 7,778,680. U.S. application Ser. No. 11/334,876 is a continuation-in-part of U.S. application Ser. No. 11/007,920 filed Dec. 8, 2004 now U.S. Pat. No. 8,282,549, which claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 60/528,382 filed Dec. 9, 2003, U.S. Provisional Application No. 60/587,787 filed Jul. 13, 2004, and U.S. Provisional Application No. 60/614,683 filed Sep. 30, 2004. U.S. application Ser. No. 11/334,876 is a continuation-in-part of U.S. application Ser. No. 10/991,966 filed Nov. 17, 2004, now U.S. Pat. No. 7,519,408 which claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 60/523,840 filed Nov. 19, 2003, U.S. Provisional Application No. 60/587,787 filed Jul. 13, 2004, and U.S. Provisional Application No. 60/614,683 filed Sep. 30, 2004. U.S. application Ser. No. 11/334,876 is a continuation-in-part of U.S. application Ser. No. 11/077,715 filed Mar. 10, 2005, now U.S. Pat. No. 7,497,827 which claims priority under 35 U.S.C. §119(e) to the U.S. Provisional No. 60/587,787 filed on Jul. 13, 2004, U.S. Provisional Application No. 60/587,800 filed Jul. 13, 2004, U.S. Provisional No. 60/614,683 filed Sep. 30, 2004, and U.S. Provisional Application No. 60/614,764 filed Sep. 30, 2004. Each of the above-referenced applications is hereby incorporated by reference herein in its entirety, and each of the above-referenced applications is hereby made a part of this specification.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for transcutaneous measurement of glucose in a host.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot create sufficient insulin (Type I or insulin dependent) and/or in which insulin is not effective (Type 2 or non-insulin dependent). In the diabetic state, the victim suffers from high blood sugar, which can cause an array of physiological derangements associated with the deterioration of small blood vessels, for example, kidney failure, skin ulcers, or bleeding into the vitreous of the eye. A hypoglycemic reaction (low blood sugar) can be induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring blood glucose (SMBG) monitor, which typically requires uncomfortable finger pricking methods. Due to the lack of comfort and convenience, a person with diabetes normally only measures his or her glucose levels two to four times per day. Unfortunately, such time intervals are so far spread apart that the person with diabetes likely finds out too late of a hyperglycemic or hypoglycemic condition, sometimes incurring dangerous side effects. It is not only unlikely that a person with diabetes will take a timely SMBG value, it is also likely that he or she will not know if his or her blood glucose value is going up (higher) or down (lower) based on conventional method. This inhibits the ability to make educated insulin therapy decisions.

SUMMARY OF THE INVENTION

In a first aspect, a system for monitoring a glucose concentration in a host is provided, the system comprising a continuous glucose sensor configured to produce a signal indicative of a glucose concentration in a host; and a receiver operably connected to the sensor, wherein the receiver comprises a user interface, and wherein the receiver further comprises programming configured to calibrate the signal, to display a graphical representation of the calibrated signal on the user interface, and to display a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.

In an embodiment of the first aspect, the receiver comprises programming configured to display the direction and the rate of change of the calibrated signal by a rotation of the directional arrow with a resolution of from about 1 degree to about 45 degrees.

In an embodiment of the first aspect, the directional arrow is indicative of a direction and a rate of change over a predetermined time period.

In an embodiment of the first aspect, predetermined time period is at least about 15 minutes.

In an embodiment of the first aspect, the receiver comprises programming configured to display at least one of a boundary representative of an upper glucose threshold and a boundary representative of a lower glucose threshold on the user interface.

In an embodiment of the first aspect, the receiver comprises programming configured to permit a user to set the upper glucose threshold and the lower glucose threshold.

In an embodiment of the first aspect, the system further comprises an alarm configured to provide at least one of a visual signal, an audible signal, and a tactile signal when the calibrated signal is below the lower glucose threshold.

In an embodiment of the first aspect, the system further comprises an alarm configured to provide at least one of a visual signal, an audible signal, and a tactile signal when the calibrated signal is above the upper glucose threshold.

In an embodiment of the first aspect, the receiver comprises programming configured to estimate glucose data for a future time.

In an embodiment of the first aspect, the system further comprises an alarm configured to provide at least one of a visual signal, an audible signal, and a tactile signal when the estimated glucose data for the future time is above a predetermined threshold.

In an embodiment of the first aspect, the system further comprises an alarm configured to provide at least one of a visual signal, an audible signal, and a tactile signal when the estimated glucose data for the future time is below a predetermined threshold.

In an embodiment of the first aspect, the receiver further comprises a single point glucose measuring device, wherein the single point glucose measuring device is built into the receiver, and wherein the single-point glucose measuring device is configured to receive a biological sample from the host and to measure a concentration of glucose in the biological sample, wherein the measured glucose concentration in the biological sample is reference data.

In an embodiment of the first aspect, the programming configured to calibrate the signal is configured to calibrate the signal at least in part based on the reference data.

In an embodiment of the first aspect, the receiver comprises programming configured to confirm at least one of the signal and the calibrated signal at least in part based on the reference data.

In a second aspect, a device is provided comprising a computer readable memory, the computer readable memory comprising code for processing a signal from a continuous glucose measuring device, wherein the code comprises instructions configured to process a signal received from the continuous glucose measuring device; instructions configured to calibrate the signal; instructions configured to calculate a rate of change of the calibrated signal; and instructions configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on a user interface.

In an embodiment of the second aspect, the device further comprises instructions configured to display at least one of a boundary representative of an upper glucose threshold and a boundary representative of a lower glucose threshold on the user interface.

In an embodiment of the second aspect, the device further comprises instructions configured allow at least one of the upper glucose threshold and the lower glucose threshold to be modified by a user.

In an embodiment of the second aspect, the device further comprises instructions configured to provide an alarm comprising at least one of a visual signal, an audible signal, and a tactile signal when the calibrated signal is below the lower glucose threshold.

In an embodiment of the second aspect, the device further comprises instructions configured to provide an alarm comprising at least one of a visual signal, an audible signal, and a tactile signal when the calibrated signal is above the upper glucose threshold.

In an embodiment of the second aspect, the device further comprises instructions configured to estimate glucose data for a future time.

In an embodiment of the second aspect, the device further comprises instructions configured to provide an alarm comprising at least one of a visual signal, an audible signal, and a tactile signal when the estimated glucose data for the future time is above a predetermined threshold.

In an embodiment of the second aspect, the device further comprises instructions configured to provide an alarm comprising at least one of a visual signal, an audible signal, and a tactile signal when the estimated glucose data for the future time is below a predetermined threshold.

In a third aspect, a method is provided for displaying data from a continuous glucose measuring device, the method comprising generating a signal from a continuous glucose measuring device indicative of a glucose concentration in a host; calibrating the signal; and displaying, substantially in real-time, a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal.

In an embodiment of the third aspect, the method further comprises alarming the host when the calibrated signal is below a predetermined threshold, wherein the alarm comprises at least one of a visual signal, an audible signal, and a tactile signal.

In an embodiment of the third aspect, the method further comprises alarming the host when the calibrated signal is above a predetermined threshold, wherein the alarm comprises at least one of a visual signal, an audible signal, and a tactile signal.

In an embodiment of the third aspect, the method further comprises estimating glucose data for a future time.

In an embodiment of the third aspect, the method further comprises alarming the host when the estimated glucose data for the future time is above a predetermined threshold, wherein the alarm comprises at least one of a visual signal, an audible signal, and a tactile signal.

In an embodiment of the third aspect, the method further comprises alarming the host when the estimated glucose data for the future time is below a predetermined threshold, wherein the alarm comprises at least one of a visual signal, an audible signal, and a tactile signal.

In a fourth aspect, a system is provided for monitoring glucose concentration in a host, the system comprising a continuous glucose sensor configured to produce a signal indicative of a glucose concentration in a host; and a receiver comprising an alarm, wherein the receiver is operably connected to the sensor, wherein the receiver further comprises programming configured to estimate glucose data for a future time, and wherein the receiver comprises programming further configured to trigger the alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.

In an embodiment of the fourth aspect, the alarm is at least one of a visual alarm, an audible alarm, and a tactile alarm.

In an embodiment of the fourth aspect, the predetermined threshold is user configurable.

In an embodiment of the fourth aspect, the future time is at least about 5 minutes in the future.

In an embodiment of the fourth aspect, the future time is at least about 10 minutes in the future.

In an embodiment of the fourth aspect, the future time is at least about 15 minutes in the future.

In an embodiment of the fourth aspect, the future time is at least about 20 minutes in the future.

In an embodiment of the fourth aspect, the receiver comprises programming further configured to calibrate the signal using a conversion function, and wherein the programming configured to estimate glucose data for a future time is configured use the conversion function to extrapolate glucose data for the future time.

In an embodiment of the fourth aspect, the conversion function is calculated from a linear regression.

In an embodiment of the fourth aspect, the conversion function is calculated from a non-linear regression.

In an embodiment of the fourth aspect, the conversion function is calculated from reference data obtained from a single point glucose measuring device.

In an embodiment of the fourth aspect, the single point glucose measuring device is built into the receiver.

In an embodiment of the fourth aspect, the receiver comprises programming configured to filter the signal, and wherein the estimated glucose data is calculated from the filtered signal.

In an embodiment of the fourth aspect, the receiver comprises programming configured to apply at least one boundary to the estimated glucose data for the future time.

In an embodiment of the fourth aspect, the boundary is a physiological boundary.

In an embodiment of the fourth aspect, the receiver further comprises a user interface, wherein the receiver comprises programming further configured to calibrate the signal, and wherein the receiver comprises programming configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.

In a fifth aspect, a device is provided comprising a computer readable memory, the computer readable memory comprising code for processing data from a continuous glucose measuring device, wherein the code comprises instructions configured to process a signal received from a continuous glucose measuring device; instructions configured to estimate glucose data for a future time; and instructions configured to trigger an alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.

In an embodiment of the fifth aspect, the device further comprises instructions configured to allow a user to modify the predetermined threshold.

In an embodiment of the fifth aspect, the future time is at least about 5 minutes in the future.

In an embodiment of the fifth aspect, the future time is at least about 15 minutes in the future.

In an embodiment of the fifth aspect, the device further comprises instructions configured to calibrate the signal using a conversion function, and wherein the instructions configured to estimate glucose data for a future time are configured use the conversion function to extrapolate glucose data for the future time.

In an embodiment of the fifth aspect, the conversion function is calculated from a linear regression.

In an embodiment of the fifth aspect, the conversion function is calculated from a non-linear regression.

In an embodiment of the fifth aspect, the conversion function is calculated from reference data obtained from a single point glucose measuring device.

In an embodiment of the fifth aspect, the single point glucose measuring device is integral with the device.

In an embodiment of the fifth aspect, the device further comprises instructions configured to filter the signal, and wherein the instructions configured to estimate glucose data estimate glucose data from the filtered signal.

In an embodiment of the fifth aspect, the device further comprises instructions configured to apply at least one boundary to the estimated glucose data for the future time.

In an embodiment of the fifth aspect, the boundary is a physiological boundary.

In an embodiment of the fifth aspect, the device further comprises instructions configured to calibrate the signal and instructions configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on a user interface.

In a sixth aspect, a method is provided for monitoring a glucose concentration in a host, the method comprising generating a signal from a continuous glucose measuring device indicative of a glucose concentration in a host; processing the signal to estimate glucose data for a future time; and alarming the host when the estimated glucose data for the future time is above or below at least one predetermined threshold.

In an embodiment of the sixth aspect, the step of alarming comprises providing at least one of a visual signal, an audible signal, and a tactile signal.

In an embodiment of the sixth aspect, the method further comprises calibrating the signal using a conversion function, wherein the step of processing the signal to estimate glucose data for a future time is configured use the conversion function to extrapolate glucose data for the future time.

In an embodiment of the sixth aspect, the method further comprises a step of filtering the signal, wherein the step of processing the signal to estimate glucose data estimates the glucose data from the filtered signal.

In an embodiment of the sixth aspect, the method further comprises applying a boundary to the estimated glucose data for the future time.

In an embodiment of the sixth aspect, the method further comprises calibrating the signal and displaying a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.

In a seventh aspect, a system is provided for monitoring a glucose concentration in a host, the system comprising a continuous glucose sensor configured to produce a signal indicative of a glucose concentration in a host; and a receiver operably connected to the sensor, wherein the receiver comprises a single point glucose measuring device, wherein the single point glucose measuring device is built into the receiver, and wherein the single point glucose measuring device is configured to receive a biological sample from the host and to measure a concentration of glucose in the biological sample, wherein the measured glucose concentration in the biological sample comprises reference data, and wherein the receiver further comprises programming configured to calibrate or confirm the signal based at least in part on the reference data.

In an embodiment of the seventh aspect, the receiver comprises programming configured to calibrate and confirm the signal based at least in part on the reference data.

In an embodiment of the seventh aspect, the receiver comprises programming configured to calibrate the signal only when a rate of change of the signal is less than a predetermined threshold.

In an embodiment of the seventh aspect, the signal is a calibrated signal and wherein the predetermined threshold is 2 mg/dL/min.

In an embodiment of the seventh aspect, the receiver comprises programming configured to evaluate an accuracy of the reference data as compared to time-corresponding signal data.

In an embodiment of the seventh aspect, the receiver comprises programming configured to prompt the host to provide a biological sample to the single point glucose measuring device.

In an embodiment of the seventh aspect, the programming configured to prompt the host is based at least in part on events.

In an embodiment of the seventh aspect, the programming configured to prompt the host is based at least in part on timing.

In an embodiment of the seventh aspect, the receiver comprises programming configured to calibrate the signal, and wherein the programming is configured to prompt the host based at least in part a value of the calibrated signal or at least in part rate of change of the calibrated signal.

In an embodiment of the seventh aspect, the programming configured to calibrate or confirm is configured to calibrate the signal, wherein the receiver further comprises a user interface, and wherein the receiver comprises programming configured to display at least one of the calibrated signal and the reference data on the user interface.

In an embodiment of the seventh aspect, the receiver comprises programming configured to display the calibrated signal and the reference data on the user interface.

In an embodiment of the seventh aspect, the receiver comprises an alarm, wherein the receiver comprises programming configured to estimate glucose data for a future time, and wherein the receiver comprises programming further configured to trigger the alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.

In an embodiment of the seventh aspect, the receiver comprises a user interface, and wherein the programming configured to calibrate or confirm is configured to calibrate the signal, to display a graphical representation of the calibrated signal on the user interface, and to display a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.

In an eighth aspect, a device is provided comprising a computer readable memory, the computer readable memory comprising code for processing data from a continuous glucose measuring device and a single point glucose measuring device, wherein the code comprises instructions configured to process a signal received from a continuous glucose measuring device; instructions configured to measure a concentration of glucose in a biological sample received from a host, the measured glucose concentration in the sample comprising reference data; and instructions configured to calibrate or confirm the signal based at least in part on the reference data.

In an embodiment of the eighth aspect, the instructions configured to calibrate or confirm the glucose data are configured to calibrate and confirm the signal based at least in part on the reference data.

In an embodiment of the eighth aspect, the instructions configured to calibrate or confirm are configured to calibrate the signal only when a rate of change of the signal is less than a predetermined threshold.

In an embodiment of the eighth aspect, the signal is a calibrated signal and wherein the predetermined threshold is 2 mg/dL/min.

In an embodiment of the eighth aspect, the device further comprises instructions configured to evaluate an accuracy of the reference data as compared to time-corresponding signal data.

In an embodiment of the eighth aspect, the device further comprises instructions configured to prompt the host to provide a biological sample to the single point glucose measuring device.

In an embodiment of the eighth aspect, the instructions configured to prompt the host are based at least in part on events.

In an embodiment of the eighth aspect, the instructions configured to prompt the host are based at least in part on timing.

In an embodiment of the eighth aspect, the instructions configured to calibrate or confirm are configured to calibrate the signal, and wherein the instructions configured to prompt the host are based at least in part on a value of the calibrated signal or at least in part on a rate of change of the calibrated signal.

In an embodiment of the eighth aspect, the instructions configured to calibrate or confirm are configured to calibrate the signal, and wherein the device further comprises instructions configured to display at least one of the calibrated signal and the reference data on a user interface.

In an embodiment of the eighth aspect, the instructions configured to display at least one of the calibrated signal and the reference data on the user interface are configured to display the calibrated signal and the reference data on the user interface.

In an embodiment of the eighth aspect, the device further comprises instructions configured to estimate glucose data for a future time and instructions configured to trigger an alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.

In an embodiment of the eighth aspect, the instructions configured to calibrate or confirm are configured to calibrate the signal, wherein the device further comprises instructions configured to display a graphical representation of the calibrated signal on a user interface and instructions configured to display a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.

In a ninth aspect, a method for monitoring glucose concentration in a host is provided, the method comprising generating a signal from a continuous glucose measuring device indicative of a glucose concentration in a host; receiving the signal from the continuous glucose measuring device in a receiver; measuring a concentration of glucose in a biological sample in a single point glucose measuring device built into the receiver, the measured glucose concentration in the biological sample comprising reference data; and calibrating or confirming the signal based at least in part on the reference data.

In an embodiment of the ninth aspect, the step of calibrating or confirming the signal comprises calibrating and confirming the signal based at least in part on the reference data.

In an embodiment of the ninth aspect, the step of calibrating the signal is allowed only when a rate of change of the signal is less than a predetermined threshold.

In an embodiment of the ninth aspect, the step of calibrating or confirming the signal comprises calibrating the signal and wherein the predetermined threshold is 2 mg/dL/min.

In an embodiment of the ninth aspect, the method further comprises evaluating an accuracy of the reference data as compared to time-corresponding signal data.

In an embodiment of the ninth aspect, the method further comprises prompting the host through a user interface to provide a biological sample to the single point glucose measuring device.

In an embodiment of the ninth aspect, the step of calibrating or confirming the signal comprises calibrating the signal, and wherein the method further comprises displaying at least one of the calibrated signal and the reference data on a user interface.

In an embodiment of the ninth aspect, the step of displaying at least one of the calibrated signal and the reference data on a user interface comprises displaying the calibrated signal and the reference data on the user interface.

In an embodiment of the ninth aspect, the method further comprises estimating glucose data for a future time and triggering an alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.

In an embodiment of the ninth aspect, the step of calibrating or confirming the signal comprises calibrating the signal, and wherein the method further comprises displaying a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on a user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a transcutaneous analyte sensor system, including an applicator, a mounting unit, and an electronics unit.

FIG. 2 is a perspective view of a mounting unit, including the electronics unit in its functional position.

FIG. 3 is an exploded perspective view of a mounting unit, showing its individual components.

FIG. 4A is an exploded perspective view of a contact subassembly, showing its individual components.

FIG. 4B is a perspective view of an alternative contact configuration.

FIG. 4C is a perspective view of another alternative contact configuration.

FIG. 5A is an expanded cutaway view of a proximal portion of a sensor.

FIG. 5B is an expanded cutaway view of a distal portion of a sensor.

FIG. 5C is a cross-sectional view through the sensor of FIG. 5B on line C-C, showing an exposed electroactive surface of a working electrode surrounded by a membrane system.

FIG. 6 is an exploded side view of an applicator, showing the components that facilitate sensor insertion and subsequent needle retraction.

FIGS. 7A to 7D are schematic side cross-sectional views that illustrate applicator components and their cooperating relationships.

FIG. 8A is a perspective view of an applicator and mounting unit in one embodiment including a safety latch mechanism.

FIG. 8B is a side view of an applicator matingly engaged to a mounting unit in one embodiment, prior to sensor insertion.

FIG. 8C is a side view of a mounting unit and applicator depicted in the embodiment of FIG. 8B, after the plunger subassembly has been pushed, extending the needle and sensor from the mounting unit.

FIG. 8D is a side view of a mounting unit and applicator depicted in the embodiment of FIG. 8B, after the guide tube subassembly has been retracted, retracting the needle back into the applicator.

FIG. 8E is a perspective view of an applicator, in an alternative embodiment, matingly engaged to the mounting unit after to sensor insertion.

FIG. 8F is a perspective view of the mounting unit and applicator, as depicted in the alternative embodiment of FIG. 8E, matingly engaged while the electronics unit is slidingly inserted into the mounting unit.

FIG. 8G is a perspective view of the electronics unit, as depicted in the alternative embodiment of FIG. 8E, matingly engaged to the mounting unit after the applicator has been released.

FIGS. 8H and 8I are comparative top views of the sensor system shown in the alternative embodiment illustrated in FIGS. 8E to 8G as compared to the embodiments illustrated in FIGS. 8B to 8D.

FIGS. 9A to 9C are side views of an applicator and mounting unit, showing stages of sensor insertion.

FIGS. 10A and 10B are perspective and side cross-sectional views, respectively, of a sensor system showing the mounting unit immediately following sensor insertion and release of the applicator from the mounting unit.

FIGS. 11A and 11B are perspective and side cross-sectional views, respectively, of a sensor system showing the mounting unit after pivoting the contact subassembly to its functional position.

FIGS. 12A to 12C are perspective and side views, respectively, of the sensor system showing the sensor, mounting unit, and electronics unit in their functional positions.

FIG. 13 is an exploded perspective view of one exemplary embodiment of a continuous glucose sensor

FIG. 14 is a block diagram that illustrates electronics associated with a sensor system.

FIG. 15 is a perspective view of a sensor system wirelessly communicating with a receiver.

FIG. 16A illustrates a first embodiment wherein the receiver shows a numeric representation of the estimated analyte value on its user interface, which is described in more detail elsewhere herein.

FIG. 16B illustrates a second embodiment wherein the receiver shows an estimated glucose value and one hour of historical trend data on its user interface, which is described in more detail elsewhere herein.

FIG. 16C illustrates a third embodiment wherein the receiver shows an estimated glucose value and three hours of historical trend data on its user interface, which is described in more detail elsewhere herein.

FIG. 16D illustrates a fourth embodiment wherein the receiver shows an estimated glucose value and nine hours of historical trend data on its user interface, which is described in more detail elsewhere herein.

FIG. 17A is a block diagram that illustrates a configuration of a medical device including a continuous analyte sensor, a receiver, and an external device.

FIGS. 17B to 17D are illustrations of receiver liquid crystal displays showing embodiments of screen displays.

FIG. 18A is a flow chart that illustrates the initial calibration and data output of sensor data.

FIG. 18B is a perspective view of an integrated receiver housing in another embodiment, showing a single point glucose monitor including a stylus movably mounted to the integrated receiver, wherein the stylus is shown in a storage position.

FIG. 18C is a perspective view of the integrated housing of FIG. 18B, showing the stylus in a testing position.

FIG. 18D is a perspective view of a portion of the stylus of FIG. 18B, showing the sensing region.

FIG. 18E is a perspective view of the integrated receiver housing of FIG. 18B, showing the stylus loaded with a disposable film, and in its testing position.

FIG. 18F is a perspective view of a portion of the stylus of FIG. 18B, showing the sensing region with a disposable film stretched and/or disposed thereon for receiving a biological sample.

FIG. 18G is a graph that illustrates one example of using prior information for slope and baseline.

FIG. 19A is a flow chart that illustrates evaluation of reference and/or sensor data for statistical, clinical, and/or physiological acceptability.

FIG. 19B is a graph of two data pairs on a Clarke Error Grid to illustrate the evaluation of clinical acceptability in one exemplary embodiment.

FIG. 20 is a flow chart that illustrates evaluation of calibrated sensor data for aberrant values.

FIG. 21 is a flow chart that illustrates self-diagnostics of sensor data.

FIGS. 22A and 22B are graphical representations of glucose sensor data in a human obtained over approximately three days.

FIG. 23 is a graphical representation of glucose sensor data in a human obtained over approximately seven days.

FIG. 24 is a flow chart that illustrates the process of estimation of analyte values based on measured analyte values in one embodiment.

FIG. 25 is a graph that illustrates the case where estimation is triggered by an event wherein a patient's blood glucose concentration passes above a predetermined threshold.

FIG. 26 is a graph that illustrates a raw data stream and corresponding reference analyte values.

FIG. 27 is a flow chart that illustrates the process of compensating for a time lag associated with a continuous analyte sensor to provide real-time estimated analyte data output in one embodiment.

FIG. 28 is a graph that illustrates the data of FIG. 26, including reference analyte data and corresponding calibrated sensor analyte and estimated sensor analyte data, showing compensation for time lag using estimation.

FIG. 29 is a flow chart that illustrates the process of matching data pairs from a continuous analyte sensor and a reference analyte sensor in one embodiment.

FIG. 30 is a flow chart that illustrates the process of dynamic and intelligent estimation algorithm selection in one embodiment.

FIG. 31 is a graph that illustrates one case of dynamic and intelligent estimation applied to a data stream, showing first order estimation, second order estimation, and the measured values for a time period, wherein the second order estimation shows a closer correlation to the measured data than the first order estimation.

FIG. 32 is a flow chart that illustrates the process of estimating analyte values within physiological boundaries in one embodiment.

FIG. 33 is a graph that illustrates one case wherein dynamic and intelligent estimation is applied to a data stream, wherein the estimation performs regression and further applies physiological constraints to the estimated analyte data.

FIG. 34 is a flow chart that illustrates the process of dynamic and intelligent estimation and evaluation of analyte values in one embodiment.

FIG. 35 is a graph that illustrates a case wherein the selected estimative algorithm is evaluated in one embodiment, wherein a correlation is measured to determine a deviation of the measured analyte data with the selected estimative algorithm, if any.

FIG. 36 is a flow chart that illustrates the process of evaluating a variation of estimated future analyte value possibilities in one embodiment.

FIG. 37 is a graph that illustrates a case wherein a variation of estimated analyte values is based on physiological parameters.

FIG. 38 is a graph that illustrates a case wherein a variation of estimated analyte values is based on statistical parameters.

FIG. 39 is a flow chart that illustrates the process of estimating, measuring, and comparing analyte values in one embodiment.

FIG. 40 is a graph that illustrates a case wherein a comparison of estimated analyte values to time corresponding measured analyte values is used to determine correlation of estimated to measured analyte data.

FIG. 41 is an illustration of the receiver in one embodiment showing an analyte trend graph, including measured analyte values, estimated analyte values, and a zone of clinical risk.

FIG. 42 is an illustration of the receiver in one embodiment showing a gradient bar, including measured analyte values, estimated analyte values, and a zone of clinical risk.

FIG. 43 is an illustration of the receiver in one embodiment showing an analyte trend graph, including measured analyte values and one or more clinically acceptable target analyte values.

FIG. 44 is an illustration of the receiver of FIG. 43, further including estimated analyte values on the same screen.

FIG. 45 is an illustration of the receiver of FIG. 44, further including a variation of estimated analyte values and therapy recommendations on the same screen to help the user obtain the displayed target analyte values.

FIG. 46 is an illustration of the receiver in one embodiment, showing measured analyte values and a dynamic visual representation of a range of estimated analyte values based on a variation analysis.

FIG. 47 is an illustration of the receiver in another embodiment, showing measured analyte values and a visual representation of range of estimated analyte values based on a variation analysis.

FIG. 48 is an illustration of the receiver in another embodiment, showing a numerical representation of the most recent measured analyte value, a directional arrow indicating rate of change, and a secondary numerical value representing a variation of the measured analyte value.

FIG. 49 depicts a conventional display of glucose data (uniform y-axis), 9-hour trend graph.

FIG. 50 depicts a utility-driven display of glucose data (non-uniform y-axis), 9-hour trend graph.

FIG. 51 depicts a conventional display of glucose data, 7-day glucose chart.

FIG. 52 depicts a utility-driven display of glucose data, 7-day control chart, median (interquartile range) of daily glucose.

FIG. 53 is an illustration of a receiver in one embodiment that interfaces with a computer.

FIG. 54 is an illustration of a receiver in one embodiment that interfaces with a modem.

FIG. 55 is an illustration of a receiver in one embodiment that interfaces with an insulin pen.

FIG. 56 is an illustration of a receiver in one embodiment that interfaces with an insulin pump.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The following description and examples illustrate some exemplary embodiments of the disclosed invention in detail. Those of skill in the art will recognize that there are numerous variations and modifications of this invention that are encompassed by its scope. Accordingly, the description of a certain exemplary embodiment should not be deemed to limit the scope of the present invention.

Definitions

In order to facilitate an understanding of the preferred embodiments, a number of terms are defined below.

The term “analyte” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed. Analytes can include naturally occurring substances, artificial substances, metabolites, and/or reaction products. In some embodiments, the analyte for measurement by the sensing regions, devices, and methods is glucose. However, other analytes are contemplated as well, including but not limited to acarboxyprothrombin; acylcarnitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine/urocanic acid, homocysteine, phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotimidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Becker muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids/acylglycines; free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose/gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β); lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin; phytanic/pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles/mumps/rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellow fever virus); specific antigens (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain embodiments. The analyte can be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, the analyte can be introduced into the body, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbituates, methaqualone, tranquilizers such as Valium, Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body can also be analyzed, such as, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), histamine, and 5-hydroxyindoleacetic acid (FHIAA).

The term “host” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to mammals, particularly humans.

The term “exit-site” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the area where a medical device (for example, a sensor and/or needle) exits from the host's body.

The phrase “continuous (or continual) analyte sensing” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the period in which monitoring of analyte concentration is continuously, continually, and or intermittently (regularly or irregularly) performed, for example, about every 5 to 10 minutes.

The term “electrochemically reactive surface” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the surface of an electrode where an electrochemical reaction takes place. For example, a working electrode measures hydrogen peroxide produced by the enzyme-catalyzed reaction of the analyte detected, which reacts to create an electric current. Glucose analyte can be detected utilizing glucose oxidase, which produces H₂O₂ as a byproduct. H₂O₂ reacts with the surface of the working electrode, producing two protons (2H⁺), two electrons (2e⁻) and one molecule of oxygen (O₂), which produces the electronic current being detected.

The term “electronic connection” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to any electronic connection known to those in the art that can be utilized to interface the sensing region electrodes with the electronic circuitry of a device, such as mechanical (for example, pin and socket) or soldered electronic connections.

The term “interferant” and “interferants” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to species that interfere with the measurement of an analyte of interest in a sensor to produce a signal that does not accurately represent the analyte measurement. In one example of an electrochemical sensor, interferants are compounds with oxidation potentials that overlap with the analyte to be measured.

The term “sensing region” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the region of a monitoring device responsible for the detection of a particular analyte. The sensing region generally comprises a non-conductive body, a working electrode (anode), a reference electrode (optional), and/or a counter electrode (cathode) passing through and secured within the body forming electrochemically reactive surfaces on the body and an electronic connective means at another location on the body, and a multi-domain membrane affixed to the body and covering the electrochemically reactive surface.

The term “high oxygen solubility domain” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a domain composed of a material that has higher oxygen solubility than aqueous media such that it concentrates oxygen from the biological fluid surrounding the membrane system. The domain can act as an oxygen reservoir during times of minimal oxygen need and has the capacity to provide, on demand, a higher oxygen gradient to facilitate oxygen transport across the membrane. Thus, the ability of the high oxygen solubility domain to supply a higher flux of oxygen to critical domains when needed can improve overall sensor function.

The term “domain” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a region of the membrane system that can be a layer, a uniform or non-uniform gradient (for example, an anisotropic region of a membrane), or a portion of a membrane.

The phrase “distal to” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the spatial relationship between various elements in comparison to a particular point of reference. In general, the term indicates an element is located relatively far from the reference point than another element.

The term “proximal to” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the spatial relationship between various elements in comparison to a particular point of reference. In general, the term indicates an element is located relatively near to the reference point than another element.

The terms “in vivo portion” and “distal portion” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to the portion of the device (for example, a sensor) adapted for insertion into and/or existence within a living body of a host.

The terms “ex vivo portion” and “proximal portion” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to the portion of the device (for example, a sensor) adapted to remain and/or exist outside of a living body of a host.

The terms “raw data stream” and “data stream” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to an analog or digital signal from the analyte sensor directly related to the measured analyte. For example, the raw data stream is digital data in “counts” converted by an A/D converter from an analog signal (for example, voltage or amps) representative of an analyte concentration. The terms broadly encompass a plurality of time spaced data points from a substantially continuous analyte sensor, each of which comprises individual measurements taken at time intervals ranging from fractions of a second up to, for example, 1, 2, or 5 minutes or longer.

The term “count” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a unit of measurement of a digital signal. For example, a raw data stream measured in counts is directly related to a voltage (for example, converted by an A/D converter), which is directly related to current from the working electrode.

The term “physiologically feasible” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to one or more physiological parameters obtained from continuous studies of glucose data in humans and/or animals. For example, a maximal sustained rate of change of glucose in humans of about 4 to 6 mg/dL/min and a maximum acceleration of the rate of change of about 0.1 to 0.2 mg/dL/min/min are deemed physiologically feasible limits. Values outside of these limits are considered non-physiological and are likely a result of, e.g. signal error.

The term “ischemia” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to local and temporary deficiency of blood supply due to obstruction of circulation to a part (for example, a sensor). Ischemia can be caused, for example, by mechanical obstruction (for example, arterial narrowing or disruption) of the blood supply.

The term “matched data pairs” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to reference data (for example, one or more reference analyte data points) matched with substantially time corresponding sensor data (for example, one or more sensor data points).

The term “Clarke Error Grid” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an error grid analysis, for example, an error grid analysis used to evaluate the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference. See Clarke et al., “Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose” Diabetes Care, Volume 10, Number 5, September-October 1987.

The term “Consensus Error Grid” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an error grid analysis that assigns a specific level of clinical risk to any possible error between two time corresponding measurements, e.g. glucose measurements. The Consensus Error Grid is divided into zones signifying the degree of risk posed by the deviation. See Parkes et al., “A New Consensus Error Grid to Evaluate the Clinical Significance of Inaccuracies in the Measurement of Blood Glucose” Diabetes Care, Volume 23, Number 8, August 2000.

The term “clinical acceptability” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to determination of the risk of an inaccuracy to a patient. Clinical acceptability considers a deviation between time corresponding analyte measurements (for example, data from a glucose sensor and data from a reference glucose monitor) and the risk (for example, to the decision making of a person with diabetes) associated with that deviation based on the analyte value indicated by the sensor and/or reference data. An example of clinical acceptability can be 85% of a given set of measured analyte values within the “A” and “B” region of a standard Clarke Error Grid when the sensor measurements are compared to a standard reference measurement.

The term “sensor” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the component or region of a device by which an analyte can be quantified.

The term “needle” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a slender hollow instrument for introducing material into or removing material from the body.

The terms “operably connected” and “operably linked” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to one or more components linked to one or more other components. The terms can refer to a mechanical connection, an electrical connection, or a connection that allows transmission of signals between the components, e.g., wired or wirelessly. For example, one or more electrodes can be used to detect the amount of analyte in a sample and to convert that information into a signal; the signal can then be transmitted to a circuit. In such an example, the electrode is “operably linked” to the electronic circuitry.

The term “baseline” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the component of an analyte sensor signal that is not related to the analyte concentration. In one example of a glucose sensor, the baseline is composed substantially of signal contribution due to factors other than glucose (for example, interfering species, non-reaction-related hydrogen peroxide, or other electroactive species with an oxidation potential that overlaps with hydrogen peroxide). In some embodiments wherein a calibration is defined by solving for the equation y=mx+b, the value of b represents the baseline of the signal.

The terms “sensitivity” and “slope” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to an amount of electrical current produced by a predetermined amount (unit) of the measured analyte. For example, in one preferred embodiment, a sensor has a sensitivity (or slope) of about 3.5 to about 7.5 picoAmps of current for every 1 mg/dL of glucose analyte.

The term “membrane system” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a permeable or semi-permeable membrane that can be comprised of two or more domains and is typically constructed of materials of a few microns thickness or more, which is permeable to oxygen and is optionally permeable to, e.g., glucose or another analyte. In one example, the membrane system comprises an immobilized glucose oxidase enzyme, which enables a reaction to occur between glucose and oxygen whereby a concentration of glucose can be measured.

The terms “processor,” “processor module” and “microprocessor” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to a computer system, state machine, processor, or the like designed to perform arithmetic or logic operations using logic circuitry that responds to and processes the basic instructions that drive a computer.

The terms “smoothing” and “filtering” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to modification of a set of data to make it smoother and more continuous or to remove or diminish outlying points, for example, by performing a moving average of the raw data stream.

The term “algorithm” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a computational process (for example, programs) involved in transforming information from one state to another, for example, by using computer processing.

The term “regression” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to finding a line for which a set of data has a minimal measurement (for example, deviation) from that line. Regression can be linear, non-linear, first order, second order, or the like. One example of regression is least squares regression.

The term “calibration” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the process of determining the relationship between the sensor data and the corresponding reference data, which can be used to convert sensor data into meaningful values substantially equivalent to the reference data. In some embodiments, namely, in continuous analyte sensors, calibration can be updated or recalibrated over time as changes in the relationship between the sensor data and reference data occur, for example, due to changes in sensitivity, baseline, transport, metabolism, or the like.

The terms “chloridization” and “chloridizing” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to treatment or preparation with chloride. The term “chloride” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, to refer to C1⁻ ions, sources of C1⁻ ions, and salts of hydrochloric acid. Chloridization and chloridizing methods include, but are not limited to, chemical and electrochemical methods.

The term “raw data signal” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an analog or digital signal directly related to the measured analyte from the analyte sensor. In one example, the raw data signal is digital data in “counts” converted by an A/D converter from an analog signal (e.g. voltage or amps) representative of an analyte concentration.

The term “counts” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a unit of measurement of a digital signal. In one example, a raw data signal measured in counts is directly related to a voltage (converted by an A/D converter), which is directly related to current.

The term “R-value” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to one conventional way of summarizing the correlation of data; that is, a statement of what residuals (e.g. root mean square deviations) are to be expected if the data are fitted to a straight line by the a regression.

The term “data association” and “data association function” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to a statistical analysis of data and particularly its correlation to, or deviation from, from a particular curve. A data association function is used to show data association. For example, the data that forms that calibration set as described herein can be analyzed mathematically to determine its correlation to, or deviation from, a curve (e.g. line or set of lines) that defines the conversion function; this correlation or deviation is the data association. A data association function is used to determine data association. Examples of data association functions include, but are not limited to, linear regression, non-linear mapping/regression, rank (e.g., non-parametric) correlation, least mean square fit, mean absolute deviation (MAD), mean absolute relative difference. In one such example, the correlation coefficient of linear regression is indicative of the amount of data association of the calibration set that forms the conversion function, and thus the quality of the calibration.

The term “quality of calibration” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the statistical association of matched data pairs in the calibration set used to create the conversion function. For example, an R-value can be calculated for a calibration set to determine its statistical data association, wherein an R-value greater than 0.79 determines a statistically acceptable calibration quality, while an R-value less than 0.79 determines statistically unacceptable calibration quality.

The term “substantially” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to being largely but not necessarily wholly that which is specified.

The term “congruence” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the quality or state of agreeing, coinciding, or being concordant. In one example, congruence can be determined using rank correlation.

The term “concordant” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to being in agreement or harmony, and/or free from discord.

The term “estimation algorithm” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the processing involved in estimating analyte values from measured analyte values for a time period during which no data exists (e.g., for a future time period or during data gaps). This term is broad enough to include one or a plurality of algorithms and/or computations. This term is also broad enough to include algorithms or computations based on mathematical, statistical, clinical, and/or physiological information.

The terms “recursive filter” and “auto-regressive algorithm” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to an equation in which includes previous averages are part of the next filtered output. More particularly, the generation of a series of observations whereby the value of each observation is partly dependent on the values of those that have immediately preceded it. One example is a regression structure in which lagged response values assume the role of the independent variables.

The terms “velocity” and “rate of change” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to time rate of change; the amount of change divided by the time required for the change. In one embodiment, these terms refer to the rate of increase or decrease in an analyte for a certain time period.

The term “acceleration” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the rate of change of velocity with respect to time. This term is broad enough to include deceleration.

The term “variation” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a divergence or amount of change from a point, line, or set of data. In one embodiment, estimated analyte values can have a variation including a range of values outside of the estimated analyte values that represent a range of possibilities based on known physiological patterns, for example.

The term “deviation” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a statistical measure representing the difference between different data sets. The term is broad enough to encompass the deviation represented as a correlation of data.

The terms “statistical parameters” and “statistical” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to information computed from the values of a sampling of data. For example, noise or variability in data can be statistically measured.

The term “statistical variation” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to divergence or change from a point, line, or set of data based on statistical information. The term “statistical information” is broad enough to include patterns or data analysis resulting from experiments, published or unpublished, for example.

The term “clinical risk” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an identified danger or potential risk to the health of a patient based on a measured or estimated analyte concentration, its rate of change, and/or its acceleration. In one exemplary embodiment, clinical risk is determined by a measured glucose concentration above or below a threshold (for example, 80-200 mg/dL) and/or its rate of change.

The term “clinically acceptable” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an analyte concentration, rate of change, and/or acceleration associated with that measured analyte that is considered to be safe for a patient. In one exemplary embodiment, clinical acceptability is determined by a measured glucose concentration within a threshold (for example, 80-200 mg/dL) and/or its rate of change.

The terms “physiological parameters” and “physiological boundaries” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to the parameters obtained from continuous studies of physiological data in humans and/or animals. For example, a maximal sustained rate of change of glucose in humans of about 4 to 6 mg/dL/min and a maximum acceleration of the rate of change of about 0.1 to 0.2 mg/dL/min² are deemed physiologically feasible limits; values outside of these limits would be considered non-physiological. As another example, the rate of change of glucose is lowest at the maxima and minima of the daily glucose range, which are the areas of greatest risk in patient treatment, thus a physiologically feasible rate of change can be set at the maxima and minima based on continuous studies of glucose data. As a further example, it has been observed that the best solution for the shape of the curve at any point along glucose signal data stream over a certain time period (for example, about 20 to 30 minutes) is a straight line, which can be used to set physiological limits. These terms are broad enough to include physiological parameters for any analyte.

The terms “individual physiological patterns” and “individual historical patterns” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to patterns obtained by monitoring a physiological characteristic, such as glucose concentration, in a mammal over a time period. For example, continual or continuous monitoring of glucose concentration in humans can recognize a “normal” pattern of turnaround at the human's lowest glucose levels.

The term “physiological variation” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to divergence or change from a point, line, or set of data based on known physiological parameters and/or patterns.

The terms “clinical error grid,” “clinical error analysis” and “error grid analysis” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to an analysis method that assigns a specific level of clinical risk to an error between two time corresponding analyte measurements. Examples include Clarke Error Grid, Consensus Grid, mean absolute relative difference, rate grid, or other clinical cost functions.

The term “Clarke Error Grid” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference. See Clarke et al., “Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose,” Diabetes Care, Volume 10, Number 5, September-October 1987, which is incorporated by reference herein in its entirety.

The term “rate grid” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a continuous sensor generated glucose value, taking into account both single-point and rate-of-change values. One example of a rate grid is described in Kovatchev, B. P.; Gonder-Frederick, L. A.; Cox, D. J.; Clarke, W. L. Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data. Diabetes Care 2004, 27, 1922-1928.

The term “curvature formula” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mathematical formula that can be used to define a curvature of a line. Some examples of curvature formulas include Euler and Rodrigues' curvature formulas.

The term “time period” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an amount of time including a single point in time and a path (for example, range of time) that extends from a first point in time to a second point in time.

The term “measured analyte values” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an analyte value or set of analyte values for a time period for which analyte data has been measured by an analyte sensor. The term is broad enough to include data from the analyte sensor before or after data processing in the sensor and/or receiver (for example, data smoothing, calibration, or the like).

The term “estimated analyte values” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to an analyte value or set of analyte values, which have been algorithmically extrapolated from measured analyte values. Typically, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated data due to a time lag in the measured data, for example.

The term “alarm” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to audible, visual, or tactile signals that are triggered in response to detection of clinical risk to a patient. In one embodiment, hyperglycemic and hypoglycemic alarms are triggered when present or future clinical danger is assessed based on continuous analyte data.

The terms “target analyte values” and “analyte value goal” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to an analyte value or set of analyte values that are clinically acceptable. In one example, a target analyte value is visually or audibly presented to a patient in order to aid in guiding the patient in understanding how they should avoid a clinically risky analyte concentration.

The terms “therapy” and “therapy recommendations” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to the treatment of disease or disorder by any method. In one exemplary embodiment, a patient is prompted with therapy recommendations such as “inject insulin” or “consume carbohydrates” in order to avoid a clinically risky glucose concentration.

The phrase “continuous glucose sensing,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the period in which monitoring of plasma glucose concentration is continuously or continually performed, for example, at time intervals ranging from fractions of a second up to, for example, 1, 2, or 5 minutes, or longer.

The term “single point glucose monitor,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a device that can be used to measure a glucose concentration within a host at a single point in time, for example, some embodiments utilize a small volume in vitro glucose monitor that includes an enzyme membrane such as described with reference to U.S. Pat. No. 4,994,167 and U.S. Pat. No. 4,757,022. It should be understood that single point glucose monitors can measure multiple samples (for example, blood or interstitial fluid); however only one sample is measured at a time and typically requires some user initiation and/or interaction.

The term “biological sample,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, sample of a host body, for example blood, interstitial fluid, spinal fluid, saliva, urine, tears, sweat, or the like.

Sensor System

The preferred embodiments relate to the use of an analyte sensor that measures a concentration of analyte of interest or a substance indicative of the concentration or presence of the analyte. In some embodiments, the sensor is a continuous device, for example a subcutaneous, transdermal (e.g., transcutaneous), or intravascular device. In some embodiments, the device can analyze a plurality of intermittent blood samples. The analyte sensor can use any method of analyte-sensing, including enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, radiometric, or the like.

The analyte sensor uses any method, including invasive, minimally invasive, and non-invasive sensing techniques, to provide an output signal indicative of the concentration of the analyte of interest. The output signal is typically a raw signal that is used to provide a useful value of the analyte of interest to a user, such as a patient or physician, who can be using the device. Accordingly, appropriate smoothing, calibration, and evaluation methods can be applied to the raw signal and/or system as a whole to provide relevant and acceptable estimated analyte data to the user.

The methods and devices of preferred embodiments can be employed in a continuous glucose sensor that measures a concentration of glucose or a substance indicative of a concentration or presence of the glucose. However, certain methods and devices of preferred embodiments are also suitable for use in connection with non-continuous (e.g., single point measurement or finger stick) monitors, such as the OneTouch® system manufactured by Lifescan, Inc., or monitors as disclosed in U.S. Pat. Nos. 5,418,142; 5,515,170; 5,526,120; 5,922,530; 5,968,836; and 6,335,203. In some embodiments, the glucose sensor is an invasive, minimally-invasive, or non-invasive device, for example a subcutaneous, transdermal, or intravascular device. In some embodiments, the device can analyze a plurality of intermittent biological samples, such as blood, interstitial fluid, or the like. The glucose sensor can use any method of glucose-measurement, including calorimetric, enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, radiometric, or the like. In alternative embodiments, the sensor can be any sensor capable of determining the level of an analyte in the body, for example oxygen, lactase, hormones, cholesterol, medicaments, viruses, or the like.

The glucose sensor uses any suitable method to provide an output signal indicative of the concentration of the glucose. The output signal is typically a raw data stream that is used to provide a value indicative of the measured glucose concentration to a patient or doctor, for example.

One exemplary embodiment described in detail below utilizes an implantable glucose sensor. Another exemplary embodiment described in detail below utilizes a transcutaneous glucose sensor.

In one alternative embodiment, the continuous glucose sensor comprises a transcutaneous sensor such as described in U.S. Pat. No. 6,565,509 to Say et al. In another alternative embodiment, the continuous glucose sensor comprises a subcutaneous sensor such as described with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No. 6,484,046 to Say et al. In another alternative embodiment, the continuous glucose sensor comprises a refillable subcutaneous sensor such as described with reference to U.S. Pat. No. 6,512,939 to Colvin et al. In another alternative embodiment, the continuous glucose sensor comprises an intravascular sensor such as described with reference to U.S. Pat. No. 6,477,395 to Schulman et al. In another alternative embodiment, the continuous glucose sensor comprises an intravascular sensor such as described with reference to U.S. Pat. No. 6,424,847 to Mastrototaro et al. All of the above patents are incorporated in their entirety herein by reference.

Although a few exemplary embodiments of continuous glucose sensors are illustrated and described herein, it should be understood that the disclosed embodiments are applicable to any device capable of single analyte, substantially continual or substantially continuous measurement of a concentration of analyte of interest and providing an output signal that represents the concentration of that analyte.

In a first exemplary embodiment, a transcutaneous analyte sensor system is provided that includes an applicator for inserting the transdermal analyte sensor under a host's skin. The sensor system includes a sensor for sensing the analyte, wherein the sensor is associated with a mounting unit adapted for mounting on the skin of the host. The mounting unit houses the electronics unit associated with the sensor and is adapted for fastening to the host's skin. In certain embodiments, the system further includes a receiver for receiving and/or processing sensor data.

FIG. 1 is a perspective view of a transcutaneous analyte sensor system 10. In the preferred embodiment of a system as depicted in FIG. 1, the sensor includes an applicator 12, a mounting unit 14, and an electronics unit 16. The system can further include a receiver 158, such as is described in more detail with reference to FIG. 15.

The mounting unit (housing) 14 includes a base 24 adapted for mounting on the skin of a host, a sensor adapted for transdermal insertion through the skin of a host (see FIG. 4A), and one or more contacts 28 configured to provide secure electrical contact between the sensor and the electronics unit 16. The mounting unit 14 is designed to maintain the integrity of the sensor in the host so as to reduce or eliminate translation of motion between the mounting unit, the host, and/or the sensor.

In one embodiment, an applicator 12 is provided for inserting the sensor 32 through the host's skin at the appropriate insertion angle with the aid of a needle (see FIGS. 6 through 8), and for subsequent removal of the needle using a continuous push-pull action. Preferably, the applicator comprises an applicator body 18 that guides the applicator components (see FIGS. 6 through 8) and includes an applicator body base 60 configured to mate with the mounting unit 14 during insertion of the sensor into the host. The mate between the applicator body base 60 and the mounting unit 14 can use any known mating configuration, for example, a snap-fit, a press-fit, an interference-fit, or the like, to discourage separation during use. One or more release latches 30 enable release of the applicator body base 60, for example, when the applicator body base 60 is snap fit into the mounting unit 14.

The electronics unit 16 includes hardware, firmware, and/or software that enable measurement of levels of the analyte via the sensor. For example, the electronics unit 16 can comprise a potentiostat, a power source for providing power to the sensor, other components useful for signal processing, and preferably an RF module for transmitting data from the electronics unit 16 to a receiver (see FIGS. 14 to 16). Electronics can be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, or a processor. Preferably, electronics unit 16 houses the sensor electronics, which comprise systems and methods for processing sensor analyte data. Examples of systems and methods for processing sensor analyte data are described in more detail below and in U.S. Patent Publication No. US-2005-0027463-A1.

After insertion of the sensor using the applicator 12, and subsequent release of the applicator 12 from the mounting unit 14 (see FIGS. 8B to 8D), the electronics unit 16 is configured to releasably mate with the mounting unit 14 in a manner similar to that described above with reference to the applicator body base 60. The electronics unit 16 includes contacts on its backside (not shown) configured to electrically connect with the contacts 28, such as are described in more detail with reference to FIGS. 2 through 4. In one embodiment, the electronics unit 16 is configured with programming, for example initialization, calibration reset, failure testing, or the like, each time it is initially inserted into the mounting unit 14 and/or each time it initially communicates with the sensor 32.

Mounting Unit

FIG. 2 is a perspective view of a sensor system of a preferred embodiment, shown in its functional position, including a mounting unit and an electronics unit matingly engaged therein. FIGS. 8 to 10 illustrate the sensor is its functional position for measurement of an analyte concentration in a host.

In preferred embodiments, the mounting unit 14, also referred to as a housing, comprises a base 24 adapted for fastening to a host's skin. The base can be formed from a variety of hard or soft materials, and preferably comprises a low profile for minimizing protrusion of the device from the host during use. In some embodiments, the base 24 is formed at least partially from a flexible material, which is believed to provide numerous advantages over conventional transcutaneous sensors, which, unfortunately, can suffer from motion-related artifacts associated with the host's movement when the host is using the device. For example, when a transcutaneous analyte sensor is inserted into the host, various movements of the sensor (for example, relative movement between the in vivo portion and the ex vivo portion, movement of the skin, and/or movement within the host (dermis or subcutaneous)) create stresses on the device and can produce noise in the sensor signal. It is believed that even small movements of the skin can translate to discomfort and/or motion-related artifact, which can be reduced or obviated by a flexible or articulated base. Thus, by providing flexibility and/or articulation of the device against the host's skin, better conformity of the sensor system 10 to the regular use and movements of the host can be achieved. Flexibility or articulation is believed to increase adhesion (with the use of an adhesive pad) of the mounting unit 14 onto the skin, thereby decreasing motion-related artifact that can otherwise translate from the host's movements and reduced sensor performance.

FIG. 3 is an exploded perspective view of a sensor system of a preferred embodiment, showing a mounting unit, an associated contact subassembly, and an electronics unit. In some embodiments, the contacts 28 are mounted on or in a subassembly hereinafter referred to as a contact subassembly 26 (see FIG. 4A), which includes a contact holder 34 configured to fit within the base 24 of the mounting unit 14 and a hinge 38 that allows the contact subassembly 26 to pivot between a first position (for insertion) and a second position (for use) relative to the mounting unit 14, which is described in more detail with reference to FIGS. 10 and 11. The term “hinge” as used herein is a broad term and is used in its ordinary sense, including, without limitation, to refer to any of a variety of pivoting, articulating, and/or hinging mechanisms, such as an adhesive hinge, a sliding joint, and the like; the term hinge does not necessarily imply a fulcrum or fixed point about which the articulation occurs.

In certain embodiments, the mounting unit 14 is provided with an adhesive pad 8, preferably disposed on the mounting unit's back surface and preferably including a releasable backing layer 9. Thus, removing the backing layer 9 and pressing the base portion 24 of the mounting unit onto the host's skin adheres the mounting unit 14 to the host's skin. Additionally or alternatively, an adhesive pad can be placed over some or all of the sensor system after sensor insertion is complete to ensure adhesion, and optionally to ensure an airtight seal or watertight seal around the wound exit-site (or sensor insertion site) (not shown). Appropriate adhesive pads can be chosen and designed to stretch, elongate, conform to, and/or aerate the region (e.g., host's skin).

In preferred embodiments, the adhesive pad 8 is formed from spun-laced, open- or closed-cell foam, and/or non-woven fibers, and includes an adhesive disposed thereon, however a variety of adhesive pads appropriate for adhesion to the host's skin can be used, as is appreciated by one skilled in the art of medical adhesive pads. In some embodiments, a double-sided adhesive pad is used to adhere the mounting unit to the host's skin. In other embodiments, the adhesive pad includes a foam layer, for example, a layer wherein the foam is disposed between the adhesive pad's side edges and acts as a shock absorber.

In some embodiments, the surface area of the adhesive pad 8 is greater than the surface area of the mounting unit's back surface. Alternatively, the adhesive pad can be sized with substantially the same surface area as the back surface of the base portion. Preferably, the adhesive pad has a surface area on the side to be mounted on the host's skin that is greater than about 1, 1.25, 1.5, 1.75, 2, 2.25, or 2.5, times the surface area of the back surface 25 of the mounting unit base 24. Such a greater surface area can increase adhesion between the mounting unit and the host's skin, minimize movement between the mounting unit and the host's skin, and/or protect the wound exit-site (sensor insertion site) from environmental and/or biological contamination. In some alternative embodiments, however, the adhesive pad can be smaller in surface area than the back surface assuming a sufficient adhesion can be accomplished.

In some embodiments, the adhesive pad 8 is substantially the same shape as the back surface 25 of the base 24, although other shapes can also be advantageously employed, for example, butterfly-shaped, round, square, or rectangular. The adhesive pad backing can be designed for two-step release, for example, a primary release wherein only a portion of the adhesive pad is initially exposed to allow adjustable positioning of the device, and a secondary release wherein the remaining adhesive pad is later exposed to firmly and securely adhere the device to the host's skin once appropriately positioned. The adhesive pad is preferably waterproof. Preferably, a stretch-release adhesive pad is provided on the back surface of the base portion to enable easy release from the host's skin at the end of the useable life of the sensor, as is described in more detail with reference to FIGS. 9A to 9C.

In some circumstances, it has been found that a conventional bond between the adhesive pad and the mounting unit may not be sufficient, for example, due to humidity that can cause release of the adhesive pad from the mounting unit. Accordingly, in some embodiments, the adhesive pad can be bonded using a bonding agent activated by or accelerated by an ultraviolet, acoustic, radio frequency, or humidity cure. In some embodiments, a eutectic bond of first and second composite materials can form a strong adhesion. In some embodiments, the surface of the mounting unit can be pretreated utilizing ozone, plasma, chemicals, or the like, in order to enhance the bondability of the surface.

A bioactive agent is preferably applied locally at the insertion site (exit-site) prior to or during sensor insertion. Suitable bioactive agents include those which are known to discourage or prevent bacterial growth and infection, for example, anti-inflammatory agents, antimicrobials, antibiotics, or the like. It is believed that the diffusion or presence of a bioactive agent can aid in prevention or elimination of bacteria adjacent to the exit-site. Additionally or alternatively, the bioactive agent can be integral with or coated on the adhesive pad, or no bioactive agent at all is employed.

FIG. 4A is an exploded perspective view of the contact subassembly 26 in one embodiment, showing its individual components. Preferably, a watertight (waterproof or water-resistant) sealing member 36, also referred to as a sealing material, fits within a contact holder 34 and provides a watertight seal configured to surround the electrical connection at the electrode terminals within the mounting unit in order to protect the electrodes (and the respective operable connection with the contacts of the electronics unit 16) from damage due to moisture, humidity, dirt, and other external environmental factors. In one embodiment, the sealing member 36 is formed from an elastomeric material, such as silicone; however, a variety of other elastomeric or sealing materials can also be used. In alternative embodiments, the seal is designed to form an interference fit with the electronics unit and can be formed from a variety of materials, for example, flexible plastics or noble metals. One of ordinary skill in the art appreciates that a variety of designs can be employed to provide a seal surrounding the electrical contacts described herein. For example, the contact holder 34 can be integrally designed as a part of the mounting unit, rather than as a separate piece thereof. Additionally or alternatively, a sealant can be provided in or around the sensor (e.g. within or on the contact subassembly or sealing member), such as is described in more detail with reference to FIGS. 11A and 11B.

In the illustrated embodiment, the sealing member 36 is formed with a raised portion 37 surrounding the contacts 28. The raised portion 37 enhances the interference fit surrounding the contacts 28 when the electronics unit 16 is mated to the mounting unit 14. Namely, the raised portion surrounds each contact and presses against the electronics unit 16 to form a tight seal around the electronics unit.

Contacts 28 fit within the seal 36 and provide for electrical connection between the sensor 32 and the electronics unit 16. In general, the contacts are designed to ensure a stable mechanical and electrical connection of the electrodes that form the sensor 32 (see FIG. 5A to 5C) to mutually engaging contacts 28 thereon. A stable connection can be provided using a variety of known methods, for example, domed metallic contacts, cantilevered fingers, pogo pins, or the like, as is appreciated by one skilled in the art.

In preferred embodiments, the contacts 28 are formed from a conductive elastomeric material, such as a carbon black elastomer, through which the sensor 32 extends (see FIGS. 10B and 11B). Conductive elastomers are advantageously employed because their resilient properties create a natural compression against mutually engaging contacts, forming a secure press fit therewith. In some embodiments, conductive elastomers can be molded in such a way that pressing the elastomer against the adjacent contact performs a wiping action on the surface of the contact, thereby creating a cleaning action during initial connection. Additionally, in preferred embodiments, the sensor 32 extends through the contacts 28 wherein the sensor is electrically and mechanically secure by the relaxation of elastomer around the sensor (see FIGS. 7A to 7D).

In an alternative embodiment, a conductive, stiff plastic forms the contacts, which are shaped to comply upon application of pressure (for example, a leaf-spring shape). Contacts of such a configuration can be used instead of a metallic spring, for example, and advantageously avoid the need for crimping or soldering through compliant materials; additionally, a wiping action can be incorporated into the design to remove contaminants from the surfaces during connection. Non-metallic contacts can be advantageous because of their seamless manufacturability, robustness to thermal compression, non-corrosive surfaces, and native resistance to electrostatic discharge (ESD) damage due to their higher-than-metal resistance.

FIGS. 4B and 4C are perspective views of alternative contact configurations. FIG. 4B is an illustration of a narrow contact configuration. FIG. 4C is an illustration of a wide contact configuration. One skilled in the art appreciates that a variety of configurations are suitable for the contacts of the preferred embodiments, whether elastomeric, stiff plastic, or other materials are used. In some circumstances, it can be advantageous to provide multiple contact configurations (such as illustrated in FIGS. 4A to 4C) to differentiate sensors from each other. In other words, the architecture of the contacts can include one or more configurations each designed (keyed) to fit with a particular electronics unit. See section entitled “Differentiation of Sensor Systems” below, which describes systems and methods for differentiating (keying) sensor systems.

Sensor

Preferably, the sensor 32 includes a distal portion 42, also referred to as the in vivo portion, adapted to extend out of the mounting unit for insertion under the host's skin, and a proximal portion 40, also referred to as an ex vivo portion, adapted to remain above the host's skin after sensor insertion and to operably connect to the electronics unit 16 via contacts 28. Preferably, the sensor 32 includes two or more electrodes: a working electrode 44 and at least one additional electrode, which can function as a counter electrode and/or reference electrode, hereinafter referred to as the reference electrode 46. A membrane system is preferably deposited over the electrodes, such as described in more detail with reference to FIGS. 5A to 5C, below.

FIG. 5A is an expanded cutaway view of a proximal portion 40 of the sensor in one embodiment, showing working and reference electrodes. In the illustrated embodiments, the working and reference electrodes 44, 46 extend through the contacts 28 to form electrical connection therewith (see FIGS. 10B and 11B). Namely, the working electrode 44 is in electrical contact with one of the contacts 28 and the reference electrode 46 is in electrical contact with the other contact 28, which in turn provides for electrical connection with the electronics unit 16 when it is mated with the mounting unit 14. Mutually engaging electrical contacts permit operable connection of the sensor 32 to the electronics unit 16 when connected to the mounting unit 14; however other methods of electrically connecting the electronics unit 16 to the sensor 32 are also possible. In some alternative embodiments, for example, the reference electrode can be configured to extend from the sensor and connect to a contact at another location on the mounting unit (e.g. non-coaxially). Detachable connection between the mounting unit 14 and electronics unit 16 provides improved manufacturability, namely, the relatively inexpensive mounting unit 14 can be disposed of when replacing the sensor system after its usable life, while the relatively more expensive electronics unit 16 can be reused with multiple sensor systems.

In alternative embodiments, the contacts 28 are formed into a variety of alternative shapes and/or sizes. For example, the contacts 28 can be discs, spheres, cuboids, and the like. Furthermore, the contacts 28 can be designed to extend from the mounting unit in a manner that causes an interference fit within a mating cavity or groove of the electronics unit, forming a stable mechanical and electrical connection therewith.

FIG. 5B is an expanded cutaway view of a distal portion of the sensor in one embodiment, showing working and reference electrodes. In preferred embodiments, the sensor is formed from a working electrode 44 and a reference electrode 46 helically wound around the working electrode 44. An insulator 45 is disposed between the working and reference electrodes to provide necessary electrical insulation therebetween. Certain portions of the electrodes are exposed to enable electrochemical reaction thereon, for example, a window 43 can be formed in the insulator to expose a portion of the working electrode 44 for electrochemical reaction.

In preferred embodiments, each electrode is formed from a fine wire with a diameter of from about 0.001 or less to about 0.010 inches or more, for example, and is formed from, e.g. a plated insulator, a plated wire, or bulk electrically conductive material. Although the illustrated electrode configuration and associated text describe one preferred method of forming a transcutaneous sensor, a variety of known transcutaneous sensor configurations can be employed with the transcutaneous analyte sensor system of the preferred embodiments, such as are described in U.S. Pat. No. 6,695,860 to Ward et al., U.S. Pat. No. 6,565,509 to Say et al., U.S. Pat. No. 6,248,067 to Causey III, et al., and U.S. Pat. No. 6,514,718 to Heller et al.

In preferred embodiments, the working electrode comprises a wire formed from a conductive material, such as platinum, platinum-iridium, palladium, graphite, gold, carbon, conductive polymer, alloys, or the like. Although the electrodes can by formed by a variety of manufacturing techniques (bulk metal processing, deposition of metal onto a substrate, or the like), it can be advantageous to form the electrodes from plated wire (e.g. platinum on steel wire) or bulk metal (e.g. platinum wire). It is believed that electrodes formed from bulk metal wire provide superior performance (e.g. in contrast to deposited electrodes), including increased stability of assay, simplified manufacturability, resistance to contamination (e.g. which can be introduced in deposition processes), and improved surface reaction (e.g. due to purity of material) without peeling or delamination.

The working electrode 44 is configured to measure the concentration of an analyte. In an enzymatic electrochemical sensor for detecting glucose, for example, the working electrode measures the hydrogen peroxide produced by an enzyme catalyzed reaction of the analyte being detected and creates a measurable electronic current For example, in the detection of glucose wherein glucose oxidase produces hydrogen peroxide as a byproduct, hydrogen peroxide reacts with the surface of the working electrode producing two protons (2H⁺), two electrons (2e⁻) and one molecule of oxygen (O₂), which produces the electronic current being detected.

In preferred embodiments, the working electrode 44 is covered with an insulating material 45, for example, a non-conductive polymer. Dip-coating, spray-coating, vapor-deposition, or other coating or deposition techniques can be used to deposit the insulating material on the working electrode. In one embodiment, the insulating material comprises parylene, which can be an advantageous polymer coating for its strength, lubricity, and electrical insulation properties. Generally, parylene is produced by vapor deposition and polymerization of para-xylylene (or its substituted derivatives). While not wishing to be bound by theory, it is believed that the lubricious coating (e.g., parylene) on the sensors of the preferred embodiments contributes to minimal trauma and extended sensor life. FIG. 23 shows transcutaneous glucose sensor data and corresponding blood glucose values over approximately seven days in a human, wherein the transcutaneous glucose sensor data was formed with a parylene coating on at least a portion of the device. While parylene coatings are generally preferred, any suitable insulating material can be used, for example, fluorinated polymers, polyethyleneterephthalate, polyurethane, polyimide, other nonconducting polymers, or the like. Glass or ceramic materials can also be employed. Other materials suitable for use include surface energy modified coating systems such as are marketed under the trade names AMC18, AMC148, AMC141, and AMC321 by Advanced Materials Components Express of Bellafonte, Pa. In some alternative embodiments, however, the working electrode may not require a coating of insulator.

The reference electrode 46, which can function as a reference electrode alone, or as a dual reference and counter electrode, is formed from silver, silver/silver chloride, or the like. Preferably, the reference electrode 46 is juxtapositioned and/or twisted with or around the working electrode 44; however other configurations are also possible (e.g., an intradermal or on-skin reference electrode). In the illustrated embodiments, the reference electrode 46 is helically wound around the working electrode 44. The assembly of wires is then optionally coated or adhered together with an insulating material, similar to that described above, so as to provide an insulating attachment.

In some embodiments, a silver wire is formed onto the sensor as described above, and subsequently chloridized to form silver/silver chloride reference electrode. Advantageously, chloridizing the silver wire as described herein enables the manufacture of a reference electrode with optimal in vivo performance. Namely, by controlling the quantity and amount of chloridization of the silver to form silver/silver chloride, improved break-in time, stability of the reference electrode, and extended life has been shown with the preferred embodiments (see FIGS. 22 and 23). Additionally, use of silver chloride as described above allows for relatively inexpensive and simple manufacture of the reference electrode.

In embodiments wherein an outer insulator is disposed, a portion of the coated assembly structure can be stripped or otherwise removed, for example, by hand, excimer lasing, chemical etching, laser ablation, grit-blasting (e.g. with sodium bicarbonate or other suitable grit), or the like, to expose the electroactive surfaces. Alternatively, a portion of the electrode can be masked prior to depositing the insulator in order to maintain an exposed electroactive surface area. In one exemplary embodiment, grit blasting is implemented to expose the electroactive surfaces, preferably utilizing a grit material that is sufficiently hard to ablate the polymer material, while being sufficiently soft so as to minimize or avoid damage to the underlying metal electrode (e.g. a platinum electrode). Although a variety of “grit” materials can be used (e.g. sand, talc, walnut shell, ground plastic, sea salt, and the like), in some preferred embodiments, sodium bicarbonate is an advantageous grit-material because it is sufficiently hard to ablate, e.g. a parylene coating without damaging, e.g. an underlying platinum conductor. One additional advantage of sodium bicarbonate blasting includes its polishing action on the metal as it strips the polymer layer, thereby eliminating a cleaning step that might otherwise be necessary.

In the embodiment illustrated in FIG. 5B, a radial window 43 is formed through the insulating material 45 to expose a circumferential electroactive surface of the working electrode. Additionally, sections 41 of electroactive surface of the reference electrode are exposed. For example, the 41 sections of electroactive surface can be masked during deposition of an outer insulating layer or etched after deposition of an outer insulating layer.

In some applications, cellular attack or migration of cells to the sensor can cause reduced sensitivity and/or function of the device, particularly after the first day of implantation. However, when the exposed electroactive surface is distributed circumferentially about the sensor (e.g. as in a radial window), the available surface area for reaction can be sufficiently distributed so as to minimize the effect of local cellular invasion of the sensor on the sensor signal. Alternatively, a tangential exposed electroactive window can be formed, for example, by stripping only one side of the coated assembly structure. In other alternative embodiments, the window can be provided at the tip of the coated assembly structure such that the electroactive surfaces are exposed at the tip of the sensor. Other methods and configurations for exposing electroactive surfaces can also be employed.

In some embodiments, the working electrode has a diameter of from about 0.001 inches or less to about 0.010 inches or more, preferably from about 0.002 inches to about 0.008 inches, and more preferably from about 0.004 inches to about 0.005 inches. The length of the window can be from about 0.1 mm (about 0.004 inches) or less to about 2 mm (about 0.078 inches) or more, and preferably from about 0.5 mm (about 0.02 inches) to about 0.75 mm (0.03 inches). In such embodiments, the exposed surface area of the working electrode is preferably from about 0.000013 in² (0.0000839 cm²) or less to about 0.0025 in² (0.016129 cm²) or more (assuming a diameter of from about 0.001 inches to about 0.010 inches and a length of from about 0.004 inches to about 0.078 inches). The preferred exposed surface area of the working electrode is selected to produce an analyte signal with a current in the picoAmp range, such as is described in more detail elsewhere herein. However, a current in the picoAmp range can be dependent upon a variety of factors, for example the electronic circuitry design (e.g. sample rate, current draw, A/D converter bit resolution, etc.), the membrane system (e.g. permeability of the analyte through the membrane system), and the exposed surface area of the working electrode. Accordingly, the exposed electroactive working electrode surface area can be selected to have a value greater than or less than the above-described ranges taking into consideration alterations in the membrane system and/or electronic circuitry. In preferred embodiments of a glucose sensor, it can be advantageous to minimize the surface area of the working electrode while maximizing the diffusivity of glucose in order to optimize the signal-to-noise ratio while maintaining sensor performance in both high and low glucose concentration ranges.

In some alternative embodiments, the exposed surface area of the working (and/or other) electrode can be increased by altering the cross-section of the electrode itself. For example, in some embodiments the cross-section of the working electrode can be defined by a cross, star, cloverleaf, ribbed, dimpled, ridged, irregular, or other non-circular configuration; thus, for any predetermined length of electrode, a specific increased surface area can be achieved (as compared to the area achieved by a circular cross-section). Increasing the surface area of the working electrode can be advantageous in providing an increased signal responsive to the analyte concentration, which in turn can be helpful in improving the signal-to-noise ratio, for example.

In some alternative embodiments, additional electrodes can be included within the assembly, for example, a three-electrode system (working, reference, and counter electrodes) and/or an additional working electrode (e.g. an electrode which can be used to generate oxygen, which is configured as a baseline subtracting electrode, or which is configured for measuring additional analytes). U.S. Pat. No. 7,081,195 and U.S. Patent Publication No. US-2005-0143635-A1 describe some systems and methods for implementing and using additional working, counter, and/or reference electrodes. In one implementation wherein the sensor comprises two working electrodes, the two working electrodes are juxtapositioned (e.g. extend parallel to each other), around which the reference electrode is disposed (e.g. helically wound). In some embodiments wherein two or more working electrodes are provided, the working electrodes can be formed in a double-, triple-, quad-, etc. helix configuration along the length of the sensor (for example, surrounding a reference electrode, insulated rod, or other support structure). The resulting electrode system can be configured with an appropriate membrane system, wherein the first working electrode is configured to measure a first signal comprising glucose and baseline and the additional working electrode is configured to measure a baseline signal consisting of baseline only (e.g. configured to be substantially similar to the first working electrode without an enzyme disposed thereon). In this way, the baseline signal can be subtracted from the first signal to produce a glucose-only signal that is substantially not subject to fluctuations in the baseline and/or interfering species on the signal.

Although the preferred embodiments illustrate one electrode configuration including one bulk metal wire helically wound around another bulk metal wire, other electrode configurations are also contemplated. In an alternative embodiment, the working electrode comprises a tube with a reference electrode disposed or coiled inside, including an insulator therebetween. Alternatively, the reference electrode comprises a tube with a working electrode disposed or coiled inside, including an insulator therebetween. In another alternative embodiment, a polymer (e.g. insulating) rod is provided, wherein the electrodes are deposited (e.g. electro-plated) thereon. In yet another alternative embodiment, a metallic (e.g. steel) rod is provided, coated with an insulating material, onto which the working and reference electrodes are deposited. In yet another alternative embodiment, one or more working electrodes are helically wound around a reference electrode.

Preferably, the electrodes and membrane systems of the preferred embodiments are coaxially formed, namely, the electrodes and/or membrane system all share the same central axis. While not wishing to be bound by theory, it is believed that a coaxial design of the sensor enables a symmetrical design without a preferred bend radius. Namely, in contrast to prior art sensors comprising a substantially planar configuration that can suffer from regular bending about the plane of the sensor, the coaxial design of the preferred embodiments do not have a preferred bend radius and therefore are not subject to regular bending about a particular plane (which can cause fatigue failures and the like). However, non-coaxial sensors can be implemented with the sensor system of the preferred embodiments.

In addition to the above-described advantages, the coaxial sensor design of the preferred embodiments enables the diameter of the connecting end of the sensor (proximal portion) to be substantially the same as that of the sensing end (distal portion) such that the needle is able to insert the sensor into the host and subsequently slide back over the sensor and release the sensor from the needle, without slots or other complex multi-component designs.

In one such alternative embodiment, the two wires of the sensor are held apart and configured for insertion into the host in proximal but separate locations. The separation of the working and reference electrodes in such an embodiment can provide additional electrochemical stability with simplified manufacture and electrical connectivity. It is appreciated by one skilled in the art that a variety of electrode configurations can be implemented with the preferred embodiments.

In some embodiments, the sensor includes an antimicrobial portion configured to extend through the exit-site when the sensor is implanted in the host. Namely, the sensor is designed with in vivo and ex vivo portions as described in more detail elsewhere herein; additionally, the sensor comprises a transition portion, also referred to as an antimicrobial portion, located between the in vivo and ex vivo portions 42, 40. The antimicrobial portion is designed to provide antimicrobial effects to the exit-site and adjacent tissue when implanted in the host.

In some embodiments, the antimicrobial portion comprises silver, e.g., the portion of a silver reference electrode that is configured to extend through the exit-site when implanted. Although exit-site infections are a common adverse occurrence associated with some conventional transcutaneous medical devices, the devices of preferred embodiments are designed at least in part to minimize infection, to minimize irritation, and/or to extend the duration of implantation of the sensor by utilizing a silver reference electrode to extend through the exit-site when implanted in a patient. While not wishing to be bound by theory, it is believed that the silver may reduce local tissue infections (within the tissue and at the exit-site); namely, steady release of molecular quantities of silver is believed to have an antimicrobial effect in biological tissue (e.g., reducing or preventing irritation and infection), also referred to as passive antimicrobial effects. Although one example of passive antimicrobial effects is described herein, one skilled in the art can appreciate a variety of passive anti-microbial systems and methods that can be implemented with the preferred embodiments. Additionally, it is believed that antimicrobial effects can contribute to extended life of a transcutaneous analyte sensor, enabling a functional lifetime past a few days, e.g., seven days or longer. FIG. 23 shows transcutaneous glucose sensor data and corresponding blood glucose values over approximately seven days in a human, wherein the transcutaneous glucose sensor data was formed with a silver transition portion that extended through the exit-site after sensor implantation.

In some embodiments, active antimicrobial systems and methods are provided in the sensor system in order to further enhance the antimicrobial effects at the exit-site. In one such embodiment, an auxiliary silver wire is disposed on or around the sensor, wherein the auxiliary silver wire is connected to electronics and configured to pass a current sufficient to enhance its antimicrobial properties (active antimicrobial effects), as is appreciated by one skilled in the art. The current can be passed continuously or intermittently, such that sufficient antimicrobial properties are provided. Although one example of active antimicrobial effects is described herein, one skilled in the art can appreciate a variety of active anti-microbial systems and methods that can be implemented with the preferred embodiments.

Anchoring Mechanism

It is preferred that the sensor remains substantially stationary within the tissue of the host, such that migration or motion of the sensor with respect to the surrounding tissue is minimized. Migration or motion is believed to cause inflammation at the sensor implant site due to irritation, and can also cause noise on the sensor signal due to motion-related artifact, for example. Therefore, it can be advantageous to provide an anchoring mechanism that provides support for the sensor's in vivo portion to avoid the above-mentioned problems. Combining advantageous sensor geometry with an advantageous anchoring minimizes additional parts and allows for an optimally small or low profile design of the sensor. In one embodiment the sensor includes a surface topography, such as the helical surface topography provided by the reference electrode surrounding the working electrode. In alternative embodiments, a surface topography could be provided by a roughened surface, porous surface (e.g. porous parylene), ridged surface, or the like. Additionally (or alternatively), the anchoring can be provided by prongs, spines, barbs, wings, hooks, a bulbous portion (for example, at the distal end), an S-bend along the sensor, a rough surface topography, a gradually changing diameter, combinations thereof, or the like, which can be used alone or in combination with the helical surface topography to stabilize the sensor within the subcutaneous tissue.

Variable Stiffness

As described above, conventional transcutaneous devices are believed to suffer from motion artifact associated with host movement when the host is using the device. For example, when a transcutaneous analyte sensor is inserted into the host, various movements on the sensor (for example, relative movement within and between the subcutaneous space, dermis, skin, and external portions of the sensor) create stresses on the device, which is known to produce artifacts on the sensor signal. Accordingly, there are different design considerations (for example, stress considerations) on various sections of the sensor. For example, the distal portion 42 of the sensor can benefit in general from greater flexibility as it encounters greater mechanical stresses caused by movement of the tissue within the patient and relative movement between the in vivo and ex vivo portions of the sensor. On the other hand, the proximal portion 40 of the sensor can benefit in general from a stiffer, more robust design to ensure structural integrity and/or reliable electrical connections. Additionally, in some embodiments wherein a needle is retracted over the proximal portion 40 of the device (see FIGS. 6 to 8), a stiffer design can minimize crimping of the sensor and/or ease in retraction of the needle from the sensor. Thus, by designing greater flexibility into the in vivo (distal) portion 42, the flexibility is believed to compensate for patient movement, and noise associated therewith. By designing greater stiffness into the ex vivo (proximal) portion 40, column strength (for retraction of the needle over the sensor), electrical connections, and integrity can be enhanced. In some alternative embodiments, a stiffer distal end and/or a more flexible proximal end can be advantageous as described in U.S. Publication No. US-2006-0015024-A1.

The preferred embodiments provide a distal portion 42 of the sensor 32 designed to be more flexible than a proximal portion 40 of the sensor. The variable stiffness of the preferred embodiments can be provided by variable pitch of any one or more helically wound wires of the device, variable cross-section of any one or more wires of the device, and/or variable hardening and/or softening of any one or more wires of the device, such as is described in more detail with reference to U.S. Publication No. US-2006-0015024-A1.

Membrane System

FIG. 5C is a cross-sectional view through the sensor on line C-C of FIG. 5B showing the exposed electroactive surface of the working electrode surrounded by the membrane system in one embodiment. Preferably, a membrane system is deposited over at least a portion of the electroactive surfaces of the sensor 32 (working electrode and optionally reference electrode) and provides protection of the exposed electrode surface from the biological environment, diffusion resistance (limitation) of the analyte if needed, a catalyst for enabling an enzymatic reaction, limitation or blocking of interferants, and/or hydrophilicity at the electrochemically reactive surfaces of the sensor interface. Some examples of suitable membrane systems are described in U.S. Patent Publication No. 2005-0245799-A1.

In general, the membrane system includes a plurality of domains, for example, an electrode domain 47, an interference domain 48, an enzyme domain 49 (for example, including glucose oxidase), and a resistance domain 50, and can include a high oxygen solubility domain, and/or a bioprotective domain (not shown), such as is described in more detail in U.S. Patent Publication No. 2005-0245799-A1, and such as is described in more detail below. The membrane system can be deposited on the exposed electroactive surfaces using known thin film techniques (for example, spraying, electro-depositing, dipping, or the like). In one embodiment, one or more domains are deposited by dipping the sensor into a solution and drawing out the sensor at a speed that provides the appropriate domain thickness. However, the membrane system can be disposed over (or deposited on) the electroactive surfaces using any known method as will be appreciated by one skilled in the art.

Electrode Domain

In some embodiments, the membrane system comprises an optional electrode domain 47. The electrode domain 47 is provided to ensure that an electrochemical reaction occurs between the electroactive surfaces of the working electrode and the reference electrode, and thus the electrode domain 47 is preferably situated more proximal to the electroactive surfaces than the enzyme domain. Preferably, the electrode domain 47 includes a semipermeable coating that maintains a layer of water at the electrochemically reactive surfaces of the sensor, for example, a humectant in a binder material can be employed as an electrode domain; this allows for the full transport of ions in the aqueous environment. The electrode domain can also assist in stabilizing the operation of the sensor by overcoming electrode start-up and drifting problems caused by inadequate electrolyte. The material that forms the electrode domain can also protect against pH-mediated damage that can result from the formation of a large pH gradient due to the electrochemical activity of the electrodes.

In one embodiment, the electrode domain 47 includes a flexible, water-swellable, hydrogel film having a “dry film” thickness of from about 0.05 micron or less to about 20 microns or more, more preferably from about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns, and more preferably from about 2, 2.5 or 3 microns to about 3.5, 4, 4.5, or 5 microns. “Dry film” thickness refers to the thickness of a cured film cast from a coating formulation by standard coating techniques.

In certain embodiments, the electrode domain 47 is formed of a curable mixture of a urethane polymer and a hydrophilic polymer. Particularly preferred coatings are formed of a polyurethane polymer having carboxylate functional groups and non-ionic hydrophilic polyether segments, wherein the polyurethane polymer is crosslinked with a water soluble carbodiimide (e.g., 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)) in the presence of polyvinylpyrrolidone and cured at a moderate temperature of about 50° C.

Preferably, the electrode domain 47 is deposited by spray or dip-coating the electroactive surfaces of the sensor 32. More preferably, the electrode domain is formed by dip-coating the electroactive surfaces in an electrode solution and curing the domain for a time of from about 15 to about 30 minutes at a temperature of from about 40 to about 55° C. (and can be accomplished under vacuum (e.g., 20 to 30 mmHg)). In embodiments wherein dip-coating is used to deposit the electrode domain, a preferred insertion rate of from about 1 to about 3 inches per minute, with a preferred dwell time of from about 0.5 to about 2 minutes, and a preferred withdrawal rate of from about 0.25 to about 2 inches per minute provide a functional coating. However, values outside of those set forth above can be acceptable or even desirable in certain embodiments, for example, dependent upon viscosity and surface tension as is appreciated by one skilled in the art. In one embodiment, the electroactive surfaces of the electrode system are dip-coated one time (one layer) and cured at 50° C. under vacuum for 20 minutes.

Although an independent electrode domain is described herein, in some embodiments, sufficient hydrophilicity can be provided in the interference domain and/or enzyme domain (the domain adjacent to the electroactive surfaces) so as to provide for the full transport of ions in the aqueous environment (e.g. without a distinct electrode domain).

Interference Domain

In some embodiments, an optional interference domain 48 is provided, which generally includes a polymer domain that restricts the flow of one or more interferants. In some embodiments, the interference domain 48 functions as a molecular sieve that allows analytes and other substances that are to be measured by the electrodes to pass through, while preventing passage of other substances, including interferants such as ascorbate and urea (see U.S. Pat. No. 6,001,067 to Shults). Some known interferants for a glucose-oxidase based electrochemical sensor include acetaminophen, ascorbic acid, bilirubin, cholesterol, creatinine, dopamine, ephedrine, ibuprofen, L-dopa, methyldopa, salicylate, tetracycline, tolazamide, tolbutamide, triglycerides, and uric acid.

Several polymer types that can be utilized as a base material for the interference domain 48 include polyurethanes, polymers having pendant ionic groups, and polymers having controlled pore size, for example. In one embodiment, the interference domain includes a thin, hydrophobic membrane that is non-swellable and restricts diffusion of low molecular weight species. The interference domain 48 is permeable to relatively low molecular weight substances, such as hydrogen peroxide, but restricts the passage of higher molecular weight substances, including glucose and ascorbic acid. Other systems and methods for reducing or eliminating interference species that can be applied to the membrane system of the preferred embodiments are described in U.S. Pat. No. 7,074,307, U.S. Patent Publication No. US-2005-0176136-A1, U.S. Pat. No. 7,081,195 and U.S. Patent Publication No. US-2005-0143635-A1. In some alternative embodiments, a distinct interference domain is not included.

In preferred embodiments, the interference domain 48 is deposited onto the electrode domain (or directly onto the electroactive surfaces when a distinct electrode domain is not included) for a domain thickness of from about 0.05 micron or less to about 20 microns or more, more preferably from about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns, and more preferably from about 2, 2.5 or 3 microns to about 3.5, 4, 4.5, or 5 microns. Thicker membranes can also be useful, but thinner membranes are generally preferred because they have a lower impact on the rate of diffusion of hydrogen peroxide from the enzyme membrane to the electrodes. Unfortunately, the thin thickness of the interference domains conventionally used can introduce variability in the membrane system processing. For example, if too much or too little interference domain is incorporated within a membrane system, the performance of the membrane can be adversely affected.

Enzyme Domain

In preferred embodiments, the membrane system further includes an enzyme domain 49 disposed more distally situated from the electroactive surfaces than the interference domain 48 (or electrode domain 47 when a distinct interference is not included). In some embodiments, the enzyme domain is directly deposited onto the electroactive surfaces (when neither an electrode or interference domain is included). In the preferred embodiments, the enzyme domain 49 provides an enzyme to catalyze the reaction of the analyte and its co-reactant, as described in more detail below. Preferably, the enzyme domain includes glucose oxidase; however other oxidases, for example, galactose oxidase or uricase oxidase, can also be used.

For an enzyme-based electrochemical glucose sensor to perform well, the sensor's response is preferably limited by neither enzyme activity nor co-reactant concentration. Because enzymes, including glucose oxidase, are subject to deactivation as a function of time even in ambient conditions, this behavior is compensated for in forming the enzyme domain. Preferably, the enzyme domain 49 is constructed of aqueous dispersions of colloidal polyurethane polymers including the enzyme. However, in alternative embodiments the enzyme domain is constructed from an oxygen enhancing material, for example, silicone, or fluorocarbon, in order to provide a supply of excess oxygen during transient ischemia. Preferably, the enzyme is immobilized within the domain. See U.S. Patent Publication No. US-2005-0054909-A1.

In preferred embodiments, the enzyme domain 49 is deposited onto the interference domain for a domain thickness of from about 0.05 micron or less to about 20 microns or more, more preferably from about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns, and more preferably from about 2, 2.5 or 3 microns to about 3.5, 4, 4.5, or 5 microns. However in some embodiments, the enzyme domain is deposited onto the electrode domain or directly onto the electroactive surfaces. Preferably, the enzyme domain 49 is deposited by spray or dip coating. More preferably, the enzyme domain is formed by dip-coating the electrode domain into an enzyme domain solution and curing the domain for from about 15 to about 30 minutes at a temperature of from about 40 to about 55° C. (and can be accomplished under vacuum (e.g. 20 to 30 mmHg)). In embodiments wherein dip-coating is used to deposit the enzyme domain at room temperature, a preferred insertion rate of from about 1 inch per minute to about 3 inches per minute, with a preferred dwell time of from about 0.5 minutes to about 2 minutes, and a preferred withdrawal rate of from about 0.25 inch per minute to about 2 inches per minute provide a functional coating. However, values outside of those set forth above can be acceptable or even desirable in certain embodiments, for example, dependent upon viscosity and surface tension as is appreciated by one skilled in the art. In one embodiment, the enzyme domain 49 is formed by dip coating two times (namely, forming two layers) in a coating solution and curing at 50° C. under vacuum for 20 minutes. However, in some embodiments, the enzyme domain can be formed by dip-coating and/or spray-coating one or more layers at a predetermined concentration of the coating solution, insertion rate, dwell time, withdrawal rate, and/or desired thickness.

Resistance Domain

In preferred embodiments, the membrane system includes a resistance domain 50 disposed more distal from the electroactive surfaces than the enzyme domain 49. Although the following description is directed to a resistance domain for a glucose sensor, the resistance domain can be modified for other analytes and co-reactants as well.

There exists a molar excess of glucose relative to the amount of oxygen in blood; that is, for every free oxygen molecule in extracellular fluid, there are typically more than 100 glucose molecules present (see Updike et al., Diabetes Care 5:207-21(1982)). However, an immobilized enzyme-based glucose sensor employing oxygen as co-reactant is preferably supplied with oxygen in non-rate-limiting excess in order for the sensor to respond linearly to changes in glucose concentration, while not responding to changes in oxygen concentration. Specifically, when a glucose-monitoring reaction is oxygen limited, linearity is not achieved above minimal concentrations of glucose. Without a semipermeable membrane situated over the enzyme domain to control the flux of glucose and oxygen, a linear response to glucose levels can be obtained only for glucose concentrations of up to about 40 mg/dL. However, in a clinical setting, a linear response to glucose levels is desirable up to at least about 400 mg/dL.

The resistance domain 50 includes a semi permeable membrane that controls the flux of oxygen and glucose to the underlying enzyme domain 49, preferably rendering oxygen in a non-rate-limiting excess. As a result, the upper limit of linearity of glucose measurement is extended to a much higher value than that which is achieved without the resistance domain. In one embodiment, the resistance domain 50 exhibits an oxygen to glucose permeability ratio of from about 50:1 or less to about 400:1 or more, preferably about 200:1. As a result, one-dimensional reactant diffusion is adequate to provide excess oxygen at all reasonable glucose and oxygen concentrations found in the subcutaneous matrix (See Rhodes et al., Anal. Chem., 66:1520-1529 (1994)).

In alternative embodiments, a lower ratio of oxygen-to-glucose can be sufficient to provide excess oxygen by using a high oxygen solubility domain (for example, a silicone or fluorocarbon-based material or domain) to enhance the supply/transport of oxygen to the enzyme domain 49. If more oxygen is supplied to the enzyme, then more glucose can also be supplied to the enzyme without creating an oxygen rate-limiting excess. In alternative embodiments, the resistance domain is formed from a silicone composition, such as is described in U.S. Patent Publication No. US-2005-0090607-A1.

In a preferred embodiment, the resistance domain 50 includes a polyurethane membrane with both hydrophilic and hydrophobic regions to control the diffusion of glucose and oxygen to an analyte sensor, the membrane being fabricated easily and reproducibly from commercially available materials. A suitable hydrophobic polymer component is a polyurethane, or polyetherurethaneurea. Polyurethane is a polymer produced by the condensation reaction of a diisocyanate and a difunctional hydroxyl-containing material. A polyurethaneurea is a polymer produced by the condensation reaction of a diisocyanate and a difunctional amine-containing material. Preferred diisocyanates include aliphatic diisocyanates containing from about 4 to about 8 methylene units. Diisocyanates containing cycloaliphatic moieties can also be useful in the preparation of the polymer and copolymer components of the membranes of preferred embodiments. The material that forms the basis of the hydrophobic matrix of the resistance domain can be any of those known in the art as appropriate for use as membranes in sensor devices and as having sufficient permeability to allow relevant compounds to pass through it, for example, to allow an oxygen molecule to pass through the membrane from the sample under examination in order to reach the active enzyme or electrochemical electrodes. Examples of materials which can be used to make non-polyurethane type membranes include vinyl polymers, polyethers, polyesters, polyamides, inorganic polymers such as polysiloxanes and polycarbosiloxanes, natural polymers such as cellulosic and protein based materials, and mixtures or combinations thereof.

In a preferred embodiment, the hydrophilic polymer component is polyethylene oxide. For example, one useful hydrophobic-hydrophilic copolymer component is a polyurethane polymer that includes about 20% hydrophilic polyethylene oxide. The polyethylene oxide portions of the copolymer are thermodynamically driven to separate from the hydrophobic portions of the copolymer and the hydrophobic polymer component. The 20% polyethylene oxide-based soft segment portion of the copolymer used to form the final blend affects the water pick-up and subsequent glucose permeability of the membrane.

In preferred embodiments, the resistance domain 50 is deposited onto the enzyme domain 49 to yield a domain thickness of from about 0.05 microns or less to about 20 microns or more, more preferably from about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns, and more preferably from about 2, 2.5, or 3 microns to about 3.5, 4, 4.5, or 5 microns. Preferably, the resistance domain is deposited onto the enzyme domain by spray coating or dip coating. In certain embodiments, spray coating is the preferred deposition technique. The spraying process atomizes and mists the solution, and therefore most or all of the solvent is evaporated prior to the coating material settling on the underlying domain, thereby minimizing contact of the solvent with the enzyme. One additional advantage of spray-coating the resistance domain as described in the preferred embodiments includes formation of a membrane system that substantially blocks or resists ascorbate (a known electrochemical interferant in hydrogen peroxide-measuring glucose sensors). While not wishing to be bound by theory, it is believed that during the process of depositing the resistance domain as described in the preferred embodiments, a structural morphology is formed, characterized in that ascorbate does not substantially permeate therethrough.

In preferred embodiments, the resistance domain 50 is deposited on the enzyme domain 49 by spray-coating a solution of from about 1 wt. % to about 5 wt. % polymer and from about 95 wt. % to about 99 wt. % solvent. In spraying a solution of resistance domain material, including a solvent, onto the enzyme domain, it is desirable to mitigate or substantially reduce any contact with enzyme of any solvent in the spray solution that can deactivate the underlying enzyme of the enzyme domain 49. Tetrahydrofuran (THF) is one solvent that minimally or negligibly affects the enzyme of the enzyme domain upon spraying. Other solvents can also be suitable for use, as is appreciated by one skilled in the art.

Although a variety of spraying or deposition techniques can be used, spraying the resistance domain material and rotating the sensor at least one time by 180° can provide adequate coverage by the resistance domain. Spraying the resistance domain material and rotating the sensor at least two times by 120 degrees provides even greater coverage (one layer of 360° coverage), thereby ensuring resistivity to glucose, such as is described in more detail above.

In preferred embodiments, the resistance domain 50 is spray-coated and subsequently cured for a time of from about 15 to about 90 minutes at a temperature of from about 40 to about 60° C. (and can be accomplished under vacuum (e.g. 20 to 30 mmHg)). A cure time of up to about 90 minutes or more can be advantageous to ensure complete drying of the resistance domain. While not wishing to be bound by theory, it is believed that complete drying of the resistance domain aids in stabilizing the sensitivity of the glucose sensor signal. It reduces drifting of the signal sensitivity over time, and complete drying is believed to stabilize performance of the glucose sensor signal in lower oxygen environments.

In one embodiment, the resistance domain 50 is formed by spray-coating at least six layers (namely, rotating the sensor seventeen times by 120° for at least six layers of 360° coverage) and curing at 50° C. under vacuum for 60 minutes. However, the resistance domain can be formed by dip-coating or spray-coating any layer or plurality of layers, depending upon the concentration of the solution, insertion rate, dwell time, withdrawal rate, and/or the desired thickness of the resulting film.

Advantageously, sensors with the membrane system of the preferred embodiments, including an electrode domain 47 and/or interference domain 48, an enzyme domain 49, and a resistance domain 50, provide stable signal response to increasing glucose levels of from about 40 to about 400 mg/dL, and sustained function (at least 90% signal strength) even at low oxygen levels (for example, at about 0.6 mg/L O₂). While not wishing to be bound by theory, it is believed that the resistance domain provides sufficient resistivity, or the enzyme domain provides sufficient enzyme, such that oxygen limitations are seen at a much lower concentration of oxygen as compared to prior art sensors.

In preferred embodiments, a sensor signal with a current in the picoAmp range is preferred, which is described in more detail elsewhere herein. However, the ability to produce a signal with a current in the picoAmp range can be dependent upon a combination of factors, including the electronic circuitry design (e.g. A/D converter, bit resolution, and the like), the membrane system (e.g. permeability of the analyte through the resistance domain, enzyme concentration, and/or electrolyte availability to the electrochemical reaction at the electrodes), and the exposed surface area of the working electrode. For example, the resistance domain can be designed to be more or less restrictive to the analyte depending upon to the design of the electronic circuitry, membrane system, and/or exposed electroactive surface area of the working electrode.

Accordingly, in preferred embodiments, the membrane system is designed with a sensitivity of from about 1 pA/mg/dL to about 100 pA/mg/dL, preferably from about 5 pA/mg/dL to about 25 pA/mg/dL, and more preferably from about 4 pA/mg/dL to about 7 pA/mg/dL. While not wishing to be bound by any particular theory, it is believed that membrane systems designed with a sensitivity in the preferred ranges permit measurement of the analyte signal in low analyte and/or low oxygen situations. Namely, conventional analyte sensors have shown reduced measurement accuracy in low analyte ranges due to lower availability of the analyte to the sensor and/or have shown increased signal noise in high analyte ranges due to insufficient oxygen necessary to react with the amount of analyte being measured. While not wishing to be bound by theory, it is believed that the membrane systems of the preferred embodiments, in combination with the electronic circuitry design and exposed electrochemical reactive surface area design, support measurement of the analyte in the picoAmp range, which enables an improved level of resolution and accuracy in both low and high analyte ranges not seen in the prior art.

Mutarotase Enzyme

In some embodiments, mutarotase, an enzyme that converts α D-glucose to β D-glucose, is incorporated into the membrane system. Mutarotase can be incorporated into the enzyme domain and/or can be incorporated into another domain of the membrane system. In general, glucose exists in two distinct isomers, α and β, which are in equilibrium with one another in solution and in the blood or interstitial fluid. At equilibrium, α is present at a relative concentration of about 35.5% and β is present in the relative concentration of about 64.5% (see Okuda et al., Anal Biochem. 1971 September; 43(1):312-5). Glucose oxidase, which is a conventional enzyme used to react with glucose in glucose sensors, reacts with β D-glucose and not with α D-glucose. Since only the β D-glucose isomer reacts with the glucose oxidase, errant readings may occur in a glucose sensor responsive to a shift of the equilibrium between the α D-glucose and the β D-glucose. Many compounds, such as calcium, can affect equilibrium shifts of α D-glucose and β D-glucose. For example, as disclosed in U.S. Pat. No. 3,964,974 to Banaugh et al., compounds that exert a mutarotation accelerating effect on α D-glucose include histidine, aspartic acid, imidazole, glutamic acid, α hydroxyl pyridine, and phosphate.

Accordingly, a shift in α D-glucose and β D-glucose equilibrium can cause a glucose sensor based on glucose oxidase to err high or low. To overcome the risks associated with errantly high or low sensor readings due to equilibrium shifts, the sensor of the preferred embodiments can be configured to measure total glucose in the host, including α D-glucose and β D-glucose by the incorporation of the mutarotase enzyme, which converts α D-glucose to β D-glucose.

Although sensors of some embodiments described herein include an optional interference domain in order to block or reduce one or more interferants, sensors with the membrane systems of the preferred embodiments, including an electrode domain 47, an enzyme domain 48, and a resistance domain 49, have been shown to inhibit ascorbate without an additional interference domain. Namely, the membrane system of the preferred embodiments, including an electrode domain 47, an enzyme domain 48, and a resistance domain 49, has been shown to be substantially non-responsive to ascorbate in physiologically acceptable ranges. While not wishing to be bound by theory, it is believed that the processing process of spraying the depositing the resistance domain by spray coating, as described herein, forms results in a structural morphology that is substantially resistance resistant to ascorbate.

Interference-Free Membrane Systems

In general, it is believed that appropriate solvents and/or deposition methods can be chosen for one or more of the domains of the membrane system that form one or more transitional domains such that interferants do not substantially permeate therethrough. Thus, sensors can be built without distinct or deposited interference domains, which are non-responsive to interferants. While not wishing to be bound by theory, it is believed that a simplified multilayer membrane system, more robust multilayer manufacturing process, and reduced variability caused by the thickness and associated oxygen and glucose sensitivity of the deposited micron-thin interference domain can be provided. Additionally, the optional polymer-based interference domain, which usually inhibits hydrogen peroxide diffusion, is eliminated, thereby enhancing the amount of hydrogen peroxide that passes through the membrane system.

Oxygen Conduit

As described above, certain sensors depend upon an enzyme within the membrane system through which the host's bodily fluid passes and in which the analyte (for example, glucose) within the bodily fluid reacts in the presence of a co-reactant (for example, oxygen) to generate a product. The product is then measured using electrochemical methods, and thus the output of an electrode system functions as a measure of the analyte. For example, when the sensor is a glucose oxidase based glucose sensor, the species measured at the working electrode is H₂O₂. An enzyme, glucose oxidase, catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate according to the following reaction: Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule reacted there is a proportional change in the product, H₂O₂, one can monitor the change in H₂O₂ to determine glucose concentration. Oxidation of H₂O₂ by the working electrode is balanced by reduction of ambient oxygen, enzyme generated H₂O₂ and other reducible species at a counter electrode, for example. See Fraser, D. M., “An Introduction to In Vivo Biosensing: Progress and Problems.” In “Biosensors and the Body,” D. M. Fraser, ed., 1997, pp. 1-56 John Wiley and Sons, New York))

In vivo, glucose concentration is generally about one hundred times or more that of the oxygen concentration. Consequently, oxygen is a limiting reactant in the electrochemical reaction, and when insufficient oxygen is provided to the sensor, the sensor is unable to accurately measure glucose concentration. Thus, depressed sensor function or inaccuracy is believed to be a result of problems in availability of oxygen to the enzyme and/or electroactive surface(s).

Accordingly, in an alternative embodiment, an oxygen conduit (for example, a high oxygen solubility domain formed from silicone or fluorochemicals) is provided that extends from the ex vivo portion of the sensor to the in vivo portion of the sensor to increase oxygen availability to the enzyme. The oxygen conduit can be formed as a part of the coating (insulating) material or can be a separate conduit associated with the assembly of wires that forms the sensor.

Porous Biointerface Materials

In alternative embodiments, the distal portion 42 includes a porous material disposed over some portion thereof, which modifies the host's tissue response to the sensor. In some embodiments, the porous material surrounding the sensor advantageously enhances and extends sensor performance and lifetime in the short term by slowing or reducing cellular migration to the sensor and associated degradation that would otherwise be caused by cellular invasion if the sensor were directly exposed to the in vivo environment. Alternatively, the porous material can provide stabilization of the sensor via tissue ingrowth into the porous material in the long term. Suitable porous materials include silicone, polytetrafluoroethylene, expanded polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polyvinyl alcohol (PVA), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyamides, polyurethanes, cellulosic polymers, polysulfones and block copolymers thereof including, for example, di-block, tri-block, alternating, random and graft copolymers, as well as metals, ceramics, cellulose, hydrogel polymers, poly (2-hydroxyethyl methacrylate, pHEMA), hydroxyethyl methacrylate, (HEMA), polyacrylonitrile-polyvinyl chloride (PAN-PVC), high density polyethylene, acrylic copolymers, nylon, polyvinyl difluoride, polyanhydrides, poly(1-lysine), poly (L-lactic acid), hydroxyethylmethacrylate, hydroxyapeptite, alumina, zirconia, carbon fiber, aluminum, calcium phosphate, titanium, titanium alloy, nintinol, stainless steel, and CoCr alloy, or the like, such as are described in U.S. Patent Publication No. US-2005-0031689-A1 and U.S. Pat. No. 7,192,450.

In some embodiments, the porous material surrounding the sensor provides unique advantages in the short term (e.g. one to 14 days) that can be used to enhance and extend sensor performance and lifetime. However, such materials can also provide advantages in the long term too (e.g. greater than 14 days). Particularly, the in vivo portion of the sensor (the portion of the sensor that is implanted into the host's tissue) is encased (partially or fully) in a porous material. The porous material can be wrapped around the sensor (for example, by wrapping the porous material around the sensor or by inserting the sensor into a section of porous material sized to receive the sensor). Alternately, the porous material can be deposited on the sensor (for example, by electrospinning of a polymer directly thereon). In yet other alternative embodiments, the sensor is inserted into a selected section of porous biomaterial. Other methods for surrounding the in vivo portion of the sensor with a porous material can also be used as is appreciated by one skilled in the art.

The porous material surrounding the sensor advantageously slows or reduces cellular migration to the sensor and associated degradation that would otherwise be caused by cellular invasion if the sensor were directly exposed to the in vivo environment. Namely, the porous material provides a barrier that makes the migration of cells towards the sensor more tortuous and therefore slower (providing short term advantages). It is believed that this reduces or slows the sensitivity loss normally observed in a short-term sensor over time.

In an embodiment wherein the porous material is a high oxygen solubility material, such as porous silicone, the high oxygen solubility porous material surrounds some of or the entire in vivo portion 42 of the sensor. High oxygen solubility materials are materials that dynamically retain a high availability of oxygen that can be used to compensate for the local oxygen deficit during times of transient ischemia (e.g. silicone and fluorocarbons). It is believed that some signal noise normally seen by a conventional sensor can be attributed to an oxygen deficit. In one exemplary embodiment, porous silicone surrounds the sensor and thereby effectively increases the concentration of oxygen local (proximal) to the sensor. Thus, an increase in oxygen availability proximal to the sensor as achieved by this embodiment ensures that an excess of oxygen over glucose is provided to the sensor; thereby reducing the likelihood of oxygen limited reactions therein. Accordingly, by providing a high oxygen solubility material (e.g. porous silicone) surrounding the in vivo portion of the sensor, it is believed that increased oxygen availability, reduced signal noise, longevity, and ultimately enhanced sensor performance can be achieved.

Bioactive Agents

In some alternative embodiments, a bioactive agent is incorporated into the above described porous material and/or membrane system, such as is described in U.S. Patent Publication No. US-2005-0031689-A1, which diffuses out into the environment adjacent to the sensing region. Additionally or alternately, a bioactive agent can be administered locally at the exit-site or implantation-site. Suitable bioactive agents are those that modify the host's tissue response to the sensor, for example anti-inflammatory agents, anti-infective agents, anesthetics, inflammatory agents, growth factors, immunosuppressive agents, antiplatelet agents, anti-coagulants, anti-proliferates, ACE inhibitors, cytotoxic agents, anti-barrier cell compounds, vascularization-inducing compounds, anti-sense molecules, or mixtures thereof, such as are described in more detail in U.S. Patent Publication No. US-2005-0031689-A1.

In embodiments wherein the porous material is designed to enhance short-term (e.g., between about 1 and 14 days) lifetime or performance of the sensor, a suitable bioactive agent can be chosen to ensure that tissue ingrowth does not substantially occur within the pores of the porous material. Namely, by providing a tissue modifying bioactive agent, such as an anti-inflammatory agent (for example, Dexamethasone), substantially tissue ingrowth can be inhibited, at least in the short term, in order to maintain sufficient glucose transport through the pores of the porous material to maintain a stable sensitivity.

In embodiments wherein the porous material is designed to enhance long-term (e.g. between about a day to a year or more) lifetime or performance of the sensor, a suitable bioactive agent, such as a vascularization-inducing compound or anti-barrier cell compound, can be chosen to encourage tissue ingrowth without barrier cell formation.

In some alternative embodiments, the in vivo portion of the sensor is designed with porosity therethrough, for example, a design wherein the sensor wires are configured in a mesh, loose helix configuration (namely, with spaces between the wires), or with micro-fabricated holes therethrough. Porosity within the sensor modifies the host's tissue response to the sensor, because tissue ingrowth into and/or through the in vivo portion of the sensor increases stability of the sensor and/or improves host acceptance of the sensor, thereby extending the lifetime of the sensor in vivo.

In some alternative embodiments, the sensor is manufactured partially or wholly using a continuous reel-to-reel process, wherein one or more manufacturing steps are automated. In such embodiments, a manufacturing process can be provided substantially without the need for manual mounting and fixing steps and substantially without the need human interaction. A process can be utilized wherein a plurality of sensors of the preferred embodiments, including the electrodes, insulator, and membrane system, are continuously manufactured in a semi-automated or automated process.

In one embodiment, a plurality of twisted pairs is continuously formed into a coil, wherein a working electrode is coated with an insulator material around which a plurality of reference electrodes is wound. The plurality of twisted pairs are preferably indexed and subsequently moved from one station to the next whereby the membrane system is serially deposited according to the preferred embodiments. Preferably, the coil is continuous and remains as such during the entire sensor fabrication process, including winding of the electrodes, insulator application, and membrane coating processes. After drying of the membrane system, each individual sensor is cut from the continuous coil.

A continuous reel-to-reel process for manufacturing the sensor eliminates possible sensor damage due to handling by eliminating handling steps, and provides faster manufacturing due to faster trouble shooting by isolation when a product fails. Additionally, a process run can be facilitated because of elimination of steps that would otherwise be required (e.g. steps in a manual manufacturing process). Finally, increased or improved product consistency due to consistent processes within a controlled environment can be achieved in a machine or robot driven operation.

In one alternative embodiment, a continuous manufacturing process is contemplated that utilizes physical vapor deposition in a vacuum to form the sensor. Physical vapor deposition can be used to coat one or more insulating layers onto the electrodes, and further can be used to deposit the membrane system thereon. While not wishing to be bound by theory, it is believed that by implementing physical vapor deposition to form some portions or the entire sensor of the preferred embodiments, simplified manufacturing, consistent deposition, and overall increased reproducibility can be achieved.

Applicator

FIG. 6 is an exploded side view of an applicator, showing the components that enable sensor and needle insertion. In this embodiment, the applicator 12 includes an applicator body 18 that aides in aligning and guiding the applicator components. Preferably, the applicator body 18 includes an applicator body base 60 that matingly engages the mounting unit 14 and an applicator body cap 62 that enables appropriate relationships (for example, stops) between the applicator components.

The guide tube subassembly 20 includes a guide tube carrier 64 and a guide tube 66. In some embodiments, the guide tube is a cannula. The guide tube carrier 64 slides along the applicator body 18 and maintains the appropriate relative position of the guide tube 66 during insertion and subsequent retraction. For example, prior to and during insertion of the sensor, the guide tube 66 extends through the contact subassembly 26 to maintain an opening that enables easy insertion of the needle therethrough (see FIGS. 7A to 7D). During retraction of the sensor, the guide tube subassembly 20 is pulled back, engaging with and causing the needle and associated moving components to retract back into the applicator 12 (See FIGS. 7C and 7D).

A needle subassembly 68 is provided that includes a needle carrier 70 and needle 72. The needle carrier 70 cooperates with the other applicator components and carries the needle 72 between its extended and retracted positions. The needle can be of any appropriate size that can encompass the sensor 32 and aid in its insertion into the host. Preferred sizes include from about 32 gauge or less to about 18 gauge or more, more preferably from about 28 gauge to about 25 gauge, to provide a comfortable insertion for the host. Referring to the inner diameter of the needle, approximately 0.006 inches to approximately 0.023 inches is preferable, and 0.013 inches is most preferable. The needle carrier 70 is configured to engage with the guide tube carrier 64, while the needle 72 is configured to slidably nest within the guide tube 66, which allows for easy guided insertion (and retraction) of the needle through the contact subassembly 26.

A push rod subassembly 74 is provided that includes a push rod carrier 76 and a push rod 78. The push rod carrier 76 cooperates with other applicator components to ensure that the sensor is properly inserted into the host's skin, namely the push rod carrier 76 carries the push rod 78 between its extended and retracted positions. In this embodiment, the push rod 78 is configured to slidably nest within the needle 72, which allows for the sensor 32 to be pushed (released) from the needle 72 upon retraction of the needle, which is described in more detail with reference to FIGS. 7A through 7D. In some embodiments, a slight bend or serpentine shape is designed into or allowed in the sensor in order to maintain the sensor within the needle by interference. While not wishing to be bound by theory, it is believed that a slight friction fit of the sensor within the needle minimizes motion of the sensor during withdrawal of the needle and maintains the sensor within the needle prior to withdrawal of the needle.

A plunger subassembly 22 is provided that includes a plunger 80 and plunger cap 82. The plunger subassembly 22 cooperates with other applicators components to ensure proper insertion and subsequent retraction of the applicator components. In this embodiment, the plunger 80 is configured to engage with the push rod to ensure the sensor remains extended (namely, in the host) during retraction, such as is described in more detail with reference to FIG. 7C.

Sensor Insertion

FIGS. 7A through 7D are schematic side cross-sectional views that illustrate the applicator components and their cooperating relationships at various stages of sensor insertion. FIG. 7A illustrates the needle and sensor loaded prior to sensor insertion. FIG. 7B illustrates the needle and sensor after sensor insertion. FIG. 7C illustrates the sensor and needle during needle retraction. FIG. 7D illustrates the sensor remaining within the contact subassembly after needle retraction. Although the embodiments described herein suggest manual insertion and/or retraction of the various components, automation of one or more of the stages can also be employed. For example, spring-loaded mechanisms that can be triggered to automatically insert and/or retract the sensor, needle, or other cooperative applicator components can be implemented.

Referring to FIG. 7A, the sensor 32 is shown disposed within the needle 72, which is disposed within the guide tube 66. In this embodiment, the guide tube 66 is provided to maintain an opening within the contact subassembly 26 and/or contacts 28 to provide minimal friction between the needle 72 and the contact subassembly 26 and/or contacts 28 during insertion and retraction of the needle 72. However, the guide tube is an optional component, which can be advantageous in some embodiments wherein the contact subassembly 26 and/or the contacts 28 are formed from an elastomer or other material with a relatively high friction coefficient, and which can be omitted in other embodiments wherein the contact subassembly 26 and or the contacts 28 are formed from a material with a relatively low friction coefficient (for example, hard plastic or metal). A guide tube, or the like, can be preferred in embodiments wherein the contact subassembly 26 and/or the contacts 28 are formed from a material designed to frictionally hold the sensor 32 (see FIG. 7D), for example, by the relaxing characteristics of an elastomer, or the like. In these embodiments, the guide tube is provided to ease insertion of the needle through the contacts, while allowing for a frictional hold of the contacts on the sensor 32 upon subsequent needle retraction. Stabilization of the sensor in or on the contacts 28 is described in more detail with reference to FIG. 7D and following. Although FIG. 7A illustrates the needle and sensor inserted into the contacts subassembly as the initial loaded configuration, alternative embodiments contemplate a step of loading the needle through the guide tube 66 and/or contacts 28 prior to sensor insertion.

Referring to FIG. 7B, the sensor 32 and needle 72 are shown in an extended position. In this stage, the pushrod 78 has been forced to a forward position, for example by pushing on the plunger shown in FIG. 6, or the like. The plunger 22 (FIG. 6) is designed to cooperate with other of the applicator components to ensure that sensor 32 and the needle 72 extend together to a forward position (as shown); namely, the push rod 78 is designed to cooperate with other of the applicator components to ensure that the sensor 32 maintains the forward position simultaneously within the needle 72.

Referring to FIG. 7C, the needle 72 is shown during the retraction process. In this stage, the push rod 78 is held in its extended (forward) position in order to maintain the sensor 32 in its extended (forward) position until the needle 72 has substantially fully retracted from the contacts 28. Simultaneously, the cooperating applicator components retract the needle 72 and guide tube 66 backward by a pulling motion (manual or automated) thereon. In preferred embodiments, the guide tube carrier 64 (FIG. 6) engages with cooperating applicator components such that a backward (retraction) motion applied to the guide tube carrier retracts the needle 72 and guide tube 66, without (initially) retracting the push rod 78. In an alternative embodiment, the push rod 78 can be omitted and the sensor 32 held it its forward position by a cam, elastomer, or the like, which is in contact with a portion of the sensor while the needle moves over another portion of the sensor. One or more slots can be cut in the needle to maintain contact with the sensor during needle retraction.

Referring to FIG. 7D, the needle 72, guide tube 66, and push rod 78 are all retracted from contact subassembly 26, leaving the sensor 32 disposed therein. The cooperating applicator components are designed such that when the needle 72 has substantially cleared from the contacts 28 and/or contact subassembly 26, the push rod 78 is retracted along with the needle 72 and guide tube 66. The applicator 12 can then be released (manually or automatically) from the contacts 28, such as is described in more detail elsewhere herein, for example with reference to FIGS. 8D and 9A.

The preferred embodiments are generally designed with elastomeric contacts to ensure a retention force that retains the sensor 32 within the mounting unit 14 and to ensure stable electrical connection of the sensor 32 and its associated contacts 28. Although the illustrated embodiments and associated text describe the sensor 32 extending through the contacts 28 to form a friction fit therein, a variety of alternatives are contemplated. In one alternative embodiment, the sensor is configured to be disposed adjacent to the contacts (rather than between the contacts). The contacts can be constructed in a variety of known configurations, for example, metallic contacts, cantilevered fingers, pogo pins, or the like, which are configured to press against the sensor after needle retraction.

The illustrated embodiments are designed with coaxial contacts 28; namely, the contacts 28 are configured to contact the working and reference electrodes 44, 46 axially along the proximal portion 40 of the sensor 32 (see FIG. 5A). As shown in FIG. 5A, the working electrode 44 extends farther than the reference electrode 46, which allows coaxial connection of the electrodes 44, 46 with the contacts 28 at locations spaced along the promixal portion of the sensor (see also FIGS. 9B and 10B). Although the illustrated embodiments employ a coaxial design, other designs are contemplated within the scope of the preferred embodiments. For example, the reference electrode can be positioned substantially adjacent to (but spaced apart from) the working electrode at the proximal portion of the sensor. In this way, the contacts 28 can be designed side-by-side rather than co-axially along the axis of the sensor.

FIG. 8A is a perspective view of an applicator and mounting unit in one embodiment including a safety latch mechanism 84. The safety latch mechanism 84 is configured to lock the plunger subassembly 22 in a stationary position such that it cannot be accidentally pushed prior to release of the safety latch mechanism. In this embodiment, the sensor system 10 is preferably packaged (e.g. shipped) in this locked configuration, wherein the safety latch mechanism 84 holds the plunger subassembly 22 in its extended position, such that the sensor 32 cannot be prematurely inserted (e.g. accidentally released). The safety latch mechanism 84 is configured such that a pulling force shown in the direction of the arrow (see FIG. 8A) releases the lock of the safety latch mechanism on the plunger subassembly, thereby allowing sensor insertion. Although one safety latch mechanism that locks the plunger subassembly is illustrated and described herein, a variety of safety latch mechanism configurations that lock the sensor to prevent it from prematurely releasing (i.e., that lock the sensor prior to release of the safety latch mechanism) are contemplated, as can be appreciated by one skilled in the art, and fall within the scope of the preferred embodiments.

FIG. 8A additionally illustrates a force-locking mechanism 86 included in certain alternative embodiments of the sensor system, wherein the force-locking mechanism 86 is configured to ensure a proper mate between the electronics unit 16 and the mounting unit 14 (see FIG. 12A, for example). In embodiments wherein a seal is formed between the mounting unit and the electronics unit, as described in more detail elsewhere herein, an appropriate force may be required to ensure a seal has sufficiently formed therebetween; in some circumstances, it can be advantageous to ensure the electronics unit has been properly mated (e.g. snap-fit or sealingly mated) to the mounting unit. Accordingly, upon release of the applicator 12 from the mounting unit 14 (after sensor insertion), and after insertion of the electronics unit 16 into the mounting unit 14, the force-locking mechanism 86 allows the user to ensure a proper mate and/or seal therebetween. In practice, a user pivots the force-locking mechanism such that it provides force on the electronics unit 16 by pulling up on the circular tab illustrated in FIG. 8A. Although one system and one method for providing a secure and/or sealing fit between the electronics unit and the mounting unit are illustrated, various other force-locking mechanisms can be employed that utilize a variety of systems and methods for providing a secure and/or sealing fit between the electronics unit and the mounting unit (housing).

FIGS. 8B to 8D are side views of an applicator and mounting unit in one embodiment, showing various stages of sensor insertion. FIG. 8B is a side view of the applicator matingly engaged to the mounting unit prior to sensor insertion. FIG. 8C is a side view of the mounting unit and applicator after the plunger subassembly has been pushed, extending the needle and sensor from the mounting unit (namely, through the host's skin). FIG. 8D is a side view of the mounting unit and applicator after the guide tube subassembly has been retracted, retracting the needle back into the applicator. Although the drawings and associated text illustrate and describe embodiments wherein the applicator is designed for manual insertion and/or retraction, automated insertion and/or retraction of the sensor/needle, for example, using spring-loaded components, can alternatively be employed.

The preferred embodiments advantageously provide a system and method for easy insertion of the sensor and subsequent retraction of the needle in a single push-pull motion. Because of the mechanical latching system of the applicator, the user provides a continuous force on the plunger cap 82 and guide tube carrier 64 that inserts and retracts the needle in a continuous motion. When a user grips the applicator, his or her fingers grasp the guide tube carrier 64 while his or her thumb (or another finger) is positioned on the plunger cap 82. The user squeezes his or her fingers and thumb together continuously, which causes the needle to insert (as the plunger slides forward) and subsequently retract (as the guide tube carrier slides backward) due to the system of latches located within the applicator (FIGS. 6 to 8) without any necessary change of grip or force, leaving the sensor implanted in the host. In some embodiments, a continuous torque, when the applicator components are configured to rotatingly engage one another, can replace the continuous force. Some prior art sensors, in contrast to the sensors of the preferred embodiments, suffer from complex, multi-step, or multi-component insertion and retraction steps to insert and remove the needle from the sensor system.

FIG. 8B shows the mounting unit and applicator in the ready position. The sensor system can be shipped in this configuration, or the user can be instructed to mate the applicator 12 with the mounting unit 14 prior to sensor insertion. The insertion angle α is preferably fixed by the mating engagement of the applicator 12. In the illustrated embodiment, the insertion angle α is fixed in the applicator 12 by the angle of the applicator body base 60 with the shaft of the applicator body 18. However, a variety of systems and methods of ensuring proper placement can be implemented. Proper placement ensures that at least a portion of the sensor 32 extends below the dermis of the host upon insertion. In alternative embodiments, the sensor system 10 is designed with a variety of adjustable insertion angles. A variety of insertion angles can be advantageous to accommodate a variety of insertion locations and/or individual dermis configurations (for example, thickness of the dermis). In preferred embodiments, the insertion angle α is from about 0 to about 90 degrees, more preferably from about 30 to about 60 degrees, and even more preferably about 45 degrees.

In practice, the mounting unit is placed at an appropriate location on the host's skin, for example, the skin of the arm, thigh, or abdomen. Thus, removing the backing layer 9 from the adhesive pad 8 and pressing the base portion of the mounting unit on the skin adheres the mounting unit to the host's skin.

FIG. 8C shows the mounting unit and applicator after the needle 72 has been extended from the mounting unit 14 (namely, inserted into the host) by pushing the push rod subassembly 22 into the applicator 12. In this position, the sensor 32 is disposed within the needle 72 (namely, in position within the host), and held by the cooperating applicator components. In alternative embodiments, the mounting unit and/or applicator can be configured with the needle/sensor initially extended. In this way, the mechanical design can be simplified and the plunger-assisted insertion step can be eliminated or modified. The needle can be simply inserted by a manual force to puncture the host's skin, and only one (pulling) step is required on the applicator, which removes the needle from the host's skin.

FIG. 8D shows the mounting unit and applicator after the needle 72 has been retracted into the applicator 12, exposing the sensor 32 to the host's tissue. During needle retraction, the push rod subassembly maintains the sensor in its extended position (namely, within the host). In preferred embodiments, retraction of the needle irreversibly locks the needle within the applicator so that it cannot be accidentally and/or intentionally released, reinserted, or reused. The applicator is preferably configured as a disposable device to reduce or eliminate a possibility of exposure of the needle after insertion into the host. However a reusable or reloadable applicator is also contemplated in some alternative embodiments. After needle retraction, the applicator 12 can be released from the mounting unit, for example, by pressing the release latch(es) 30, and the applicator disposed of appropriately. In alternative embodiments, other mating and release configurations can be implemented between the mounting unit and the applicator, or the applicator can automatically release from the mounting unit after sensor insertion and subsequent needle retraction. In one alternative embodiment, a retention hold (e.g., ball and detent configuration) holds and releases the electronics unit (or applicator).

In one alternative embodiment, the mounting unit is configured to releasably mate with the applicator and electronics unit in a manner such that when the applicator is releasably mated to the mounting unit (e.g., after sensor insertion), the electronics unit is configured to slide into the mounting unit, thereby triggering release of the applicator and simultaneous mating of the electronics unit to the mounting unit. Cooperating mechanical components, for example, sliding ball and detent type configurations, can be used to accomplish the simultaneous mating of electronics unit and release of the applicator.

FIGS. 8E to 8G are perspective views of a sensor system 310 of an alternative embodiment, including an applicator 312, electronics unit 316, and mounting unit 314, showing various stages of applicator release and/or electronic unit mating. FIG. 8E is a perspective view of the applicator matingly engaged to the mounting unit after sensor insertion. FIG. 8F is a perspective view of the mounting unit and applicator matingly engaged while the electronics unit is slidingly inserted into the mounting unit. FIG. 8G is a perspective view of the electronics unit matingly engaged with the mounting unit after the applicator has been released.

In general, the sensor system 310 comprises a sensor adapted for transcutaneous insertion into a host's skin; a housing 314 adapted for placement adjacent to the host's skin; an electronics unit 316 releasably attachable to the housing; and an applicator 312 configured to insert the sensor through the housing 314 and into the skin of the host, wherein the applicator 312 is adapted to releasably mate with the housing 314, and wherein the system 310 is configured to release the applicator 312 from the housing when the electronics unit 316 is attached to the housing 314.

FIG. 8E shows the sensor system 310 after the sensor has been inserted and prior to release of the applicator 312. In this embodiment, the electronics unit 316 is designed to slide into the mounting unit 314. Preferably, the electronics unit 316 is configured and arranged to slide into the mounting unit 314 in only one orientation. In the illustrated embodiment, the insertion end is slightly tapered and dovetailed in order to guide insertion of the electronics unit 316 into the housing 314; however other self-alignment configurations are possible. In this way, the electronics unit 316 self-aligns and orients the electronics unit 316 in the housing, ensuring a proper fit and a secure electronic connection with the sensor.

FIG. 8F shows the sensor system 310 after the electronics unit 316 has been inserted therein. Preferably, the electronic unit 316 slide-fits into the mounting unit. In some embodiments, the sensor system 310 can be designed to allow the electronics unit 316 to be attached to the mounting unit 314 (i.e., operably connected to the sensor) before the sensor system 310 is affixed to the host. Advantageously, this design provides mechanical stability for the sensor during transmitter insertion.

FIG. 8G shows the sensor system 310 upon release of the applicator 312 from the mounting unit 314 and electronics unit 316. In this embodiment, the sensor system 310 is configured such that mating the electronics unit to the mounting unit triggers the release of the applicator 312 from the mounting unit 314.

Thus, the above described sensor system 310, also referred to as the slide-in system, allows for self-alignment of the electronics unit, creates an improved seal around the contacts due to greater holding force, provides mechanical stability for the sensor during insertion of the electronics unit, and causes automatic release of the applicator and simultaneous lock of the electronics unit into the mounting unit.

Although the overall design of the sensor system 10 results in a miniaturized volume as compared to numerous conventional devices, as described in more detail below; the sensor system 310 further enables a reduction in volume, as compared to, for example, the sensor system 10 described above.

FIGS. 8H and 8I are comparative top views of the sensor system shown in the alternative embodiment illustrated in FIGS. 8E to 8G and compared to the embodiments illustrated elsewhere (see FIGS. 1 to 3 and 10 to 12, for example). Namely, the alternative embodiment described with reference to FIGS. 8E to 8G further enables reduced size (e.g. mass, volume, and the like) of the device as compared to certain other devices. It has been discovered that the size (including volume and/or surface area) of the device can affect the function of the device. For example, motion of the mounting unit/electronics unit caused by external influences (e.g. bumping or other movement on the skin) is translated to the sensor in vivo causing motion artifact (e.g. an effect on the signal, or the like). Accordingly, by enabling a reduction of size, a more stable signal with overall improved patient comfort can be achieved.

Accordingly, slide-in system 310 described herein, including the systems and methods for inserting the sensor and connecting the electronics unit to the mounting unit, enables the mounting unit 316/electronics unit 314 subassembly to have a volume of less than about 10 cm³, more preferably less than about 8 cm³, and even more preferably less than about 6 cm³, 5 cm³, or 4 cm³ or less. In general, the mounting unit 316/electronics unit 314 subassembly comprises a first major surface and a second major surface opposite the first major surface. The first and second major surfaces together preferably account for at least about 50% of the surface area of the device; the first and second major surfaces each define a surface area, wherein the surface area of each major surface is less than or equal to about 10 cm², preferably less than or equal to about 8 cm², and more preferably less than or equal to about 6.5 cm², 6 cm², 5.5 cm², 5 cm², 4.5 cm², or 4 cm² or less. Typically, the mounting unit 316/electronics unit 314 subassembly has a length 320 of less than about 40 mm by a width 322 of less than about 20 mm and a thickness of less than about 10 mm, and more preferably a length 320 less than or equal to about 35 mm by a width 322 less than or equal to about 18 mm by a thickness of less than or equal to about 9 mm.

In some embodiments, the mounting unit 14/electronics unit 16 assembly has the following dimensional properties: preferably a length of about 6 cm or less, more preferably about 5 cm or less, more preferably still about 4.6 cm or less, even more preferably 4 cm or less, and most preferably about 3 cm or less; preferably a width of about 5 cm or less, more preferably about 4 cm or less, even more preferably 3 cm or less, even more preferably still about 2 cm or less, and most preferably about 1.5 cm or less; and/or preferably a thickness of about 2 cm or less, more preferably about 1.3 cm or less, more preferably still about 1 cm or less, even more preferably still about 0.7 cm or less, and most preferably about 0.5 cm or less. The mounting unit 14/electronics unit 16 assembly preferably has a volume of about 20 cm³ or less, more preferably about 10 cm³ or less, more preferably still about 5 cm³ or less, and most preferably about 3 cm³ or less; and preferably weighs 12 g or less, more preferably about 9 g or less, and most preferably about 6 g or less, although in some embodiments the electronics unit may weigh more than about 12 g, e.g., up to about 25 g, 45 g, or 90 g.

In some embodiments, the sensor 32 exits the base of the mounting unit 14 at a location distant from an edge of the base. In some embodiments, the sensor 32 exits the base of the mounting unit 14 at a location substantially closer to the center than the edges thereof. While not wishing to be bound by theory, it is believed that by providing an exit port for the sensor 32 located away from the edges, the sensor 32 can be protected from motion between the body and the mounting unit, snagging of the sensor by an external source, and/or environmental contaminants (e.g. microorganisms) that can migrate under the edges of the mounting unit. In some embodiments, the sensor exits the mounting unit away from an outer edge of the device. FIG. 23 shows transcutaneous glucose sensor data and corresponding blood glucose values obtained over approximately seven days in a human, wherein the transcutaneous glucose sensor data was configured with an exit port situated at a location substantially closer to the center than the edges of the base.

In some alternative embodiments, however, the sensor exits the mounting unit 14 at an edge or near an edge of the device. In some embodiments, the mounting unit is configured such that the exit port (location) of the sensor is adjustable; thus, in embodiments wherein the depth of the sensor insertion is adjustable, six-degrees of freedom can thereby be provided.

Extensible Adhesive Pad

In certain embodiments, an adhesive pad is used with the sensor system. A variety of design parameters are desirable when choosing an adhesive pad for the mounting unit. For example: 1) the adhesive pad can be strong enough to maintain full contact at all times and during all movements (devices that release even slightly from the skin have a greater risk of contamination and infection), 2) the adhesive pad can be waterproof or water permeable such that the host can wear the device even while heavily perspiring, showering, or even swimming in some cases, 3) the adhesive pad can be flexible enough to withstand linear and rotational forces due to host movements, 4) the adhesive pad can be comfortable for the host, 5) the adhesive pad can be easily releasable to minimize host pain, 6) and/or the adhesive pad can be easily releasable so as to protect the sensor during release. Unfortunately, these design parameters are difficult to simultaneously satisfy using known adhesive pads, for example, strong medical adhesive pads are available but are usually non-precise (for example, requiring significant “ripping” force during release) and can be painful during release due to the strength of their adhesion.

Therefore, the preferred embodiments provide an adhesive pad 8′ for mounting the mounting unit onto the host, including a sufficiently strong medical adhesive pad that satisfies one or more strength and flexibility requirements described above, and further provides a for easy, precise and pain-free release from the host's skin. FIG. 9A is a side view of the sensor assembly, illustrating the sensor implanted into the host with mounting unit adhered to the host's skin via an adhesive pad in one embodiment. Namely, the adhesive pad 8′ is formed from an extensible material that can be removed easily from the host's skin by stretching it lengthwise in a direction substantially parallel to (or up to about 35 degrees from) the plane of the skin. It is believed that this easy, precise, and painless removal is a function of both the high extensibility and easy stretchability of the adhesive pad.

In one embodiment, the extensible adhesive pad includes a polymeric foam layer or is formed from adhesive pad foam. It is believed that the conformability and resiliency of foam aids in conformation to the skin and flexibility during movement of the skin. In another embodiment, a stretchable solid adhesive pad, such as a rubber-based or an acrylate-based solid adhesive pad can be used. In another embodiment, the adhesive pad comprises a film, which can aid in increasing load bearing strength and rupture strength of the adhesive pad

FIGS. 9B to 9C illustrate initial and continued release of the mounting unit from the host's skin by stretching the extensible adhesive pad in one embodiment. To release the device, the backing adhesive pad is pulled in a direction substantially parallel to (or up to about 35 degrees from) the plane of the device. Simultaneously, the extensible adhesive pad stretches and releases from the skin in a relatively easy and painless manner.

In one implementation, the mounting unit is bonded to the host's skin via a single layer of extensible adhesive pad 8′, which is illustrated in FIGS. 9A to 9C. The extensible adhesive pad includes a substantially non-extensible pull-tab 52, which can include a light adhesive pad layer that allows it to be held on the mounting unit 14 prior to release. Additionally, the adhesive pad can further include a substantially non-extensible holding tab 54, which remains attached to the mounting unit during release stretching to discourage complete and/or uncontrolled release of the mounting unit from the skin.

In one alternative implementation, the adhesive pad 8′ includes two-sides, including the extensible adhesive pad and a backing adhesive pad (not shown). In this embodiment, the backing adhesive pad is bonded to the mounting unit's back surface 25 while the extensible adhesive pad 8′ is bonded to the host's skin. Both adhesive pads provide sufficient strength, flexibility, and waterproof or water permeable characteristics appropriate for their respective surface adhesion. In some embodiments, the backing and extensible adhesive pads are particularly designed with an optimized bond for their respective bonding surfaces (namely, the mounting unit and the skin).

In another alternative implementation, the adhesive pad 8′ includes a double-sided extensible adhesive pad surrounding a middle layer or backing layer (not shown). The backing layer can comprise a conventional backing film or can be formed from foam to enhance comfort, conformability, and flexibility. Preferably, each side of the double-sided adhesive pad is respectively designed for appropriate bonding surface (namely, the mounting unit and skin). A variety of alternative stretch-release configurations are possible. Controlled release of one or both sides of the adhesive pad can be facilitated by the relative lengths of each adhesive pad side, by incorporation of a non-adhesive pad zone, or the like.

FIGS. 10A and 10B are perspective and side cross-sectional views, respectively, of the mounting unit immediately following sensor insertion and release of the applicator from the mounting unit. In one embodiment, such as illustrated in FIGS. 10A and 10B, the contact subassembly 26 is held in its insertion position, substantially at the insertion angle α of the sensor. Maintaining the contact subassembly 26 at the insertion angle α during insertion enables the sensor 32 to be easily inserted straight through the contact subassembly 26. The contact subassembly 26 further includes a hinge 38 that allows movement of the contact subassembly 26 from an angled to a flat position. The term “hinge,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a mechanism that allows articulation of two or more parts or portions of a device. The term is broad enough to include a sliding hinge, for example, a ball and detent type hinging mechanism.

Although the illustrated embodiments describe a fixed insertion angle designed into the applicator, alternative embodiments can design the insertion angle into other components of the system. For example, the insertion angle can be designed into the attachment of the applicator with the mounting unit, or the like. In some alternative embodiments, a variety of adjustable insertion angles can be designed into the system to provide for a variety of host dermis configurations.

FIG. 10B illustrates the sensor 32 extending from the mounting unit 14 by a preselected distance, which defines the depth of insertion of the sensor into the host. The dermal and subcutaneous make-up of animals and humans is variable and a fixed depth of insertion may not be appropriate for all implantations. Accordingly, in an alternative embodiment, the distance that the sensor extends from the mounting unit is adjustable to accommodate a variety of host body-types. For example, the applicator 12 can be designed with a variety of adjustable settings, which control the distance that the needle 72 (and therefore the sensor 32) extends upon sensor insertion. One skilled in the art appreciates a variety of means and mechanisms can be employed to accommodate adjustable sensor insertion depths, which are considered within the scope of the preferred embodiments. The preferred insertion depth is from about 0.1 mm or less to about 2 cm or more, preferably from about 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, or 0.45 mm to about 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, or 1.9 cm.

FIGS. 11A and 11B are perspective and side cross-sectional views, respectively, of the mounting unit after articulating the contact subassembly to its functional position (which is also referred to as an inserted, implanted, or sensing position). The hinge 38 enables the contact subassembly 26 to tilt from its insertion position (FIG. 10) to its functional position (FIG. 11) by pressing downward on the contact subassembly, for example. Certain embodiments provide this pivotal movement via two separate pieces (the contact subassembly 26 and the mounting unit 14 connected by a hinge, for example, a mechanical or adhesive pad joint or hinge. A variety of pivoting, articulating, and/or hinging mechanisms can be employed with the sensors of preferred embodiments. For example, the hinge can be formed as a part of the contact subassembly 26. The contact subassembly can be formed from a flexible piece of material (such as silicone, urethane rubber, or other flexible or elastomeric material), wherein the material is sufficiently flexible to enable bending or hinging of the contact subassembly from an angle appropriate for insertion (FIGS. 10A and 10B) to a lower functional configuration (FIGS. 11A and 11B).

The relative pivotal movement of the contact subassembly is advantageous, for example, for enabling the design of a low profile device while providing support for an appropriate needle insertion angle. In its insertion position, the sensor system is designed for easy sensor insertion while forming a stable electrical connection with the associated contacts 28. In its functional position, the sensor system maintains a low profile for convenience, comfort, and discreetness during use. Thus, the sensor systems of preferred embodiments are advantageously designed with a hinging configuration to provide an optimum guided insertion angle while maintaining a low profile device during sensor use.

In some embodiments, a shock-absorbing member or feature is incorporated into the design of the sensor and configured to absorb movement of the in vivo and/or ex vivo portion of the sensor. Conventional analyte sensors can suffer from motion-related artifact associated with host movement when the host is using the device. For example, when a transcutaneous analyte sensor is inserted into the host, various movements on the sensor (for example, relative movement between the in vivo portion and the ex vivo portion and/or movement within the host) create stresses on the device and can produce noise in the sensor signal. Accordingly in some embodiments, a shock-absorbing member is located on the sensor/mounting unit in a location that absorbs stresses associated with the above-described movement.

In the preferred embodiments, the sensor 32 bends from a substantially straight to substantially bent configuration upon pivoting of the contact subassembly from the insertion to functional position. The substantially straight sensor configuration during insertion advantageously provides ease of sensor insertion, while the substantial bend in the sensor in its functional position advantageously provides stability on the proximal end of the sensor with flexibility/mobility on the distal end of the sensor. Additionally, motion within the mounting unit (e.g. caused by external forces to the mounting unit, movement of the skin, and the like) does not substantially translate to the in vivo portion of the sensor. Namely, the bend formed within the sensor 32 functions to break column strength, causing flexion that effectively absorbs movements on the sensor during use. Additionally, the sensor can be designed with a length such that when the contact subassembly 26 is pivoted to its functional position (FIG. 10B), the sensor pushes forward and flexes, allowing it to absorb motion between the in vivo and ex vivo portions of the sensor. It is believed that both of the above advantages minimize motion artifact on the sensor signal and/or minimize damage to the sensor caused by movement, both of which (motion artifact and damage) have been observed in conventional transcutaneous sensors.

In some alternative embodiments, the shock-absorbing member can be an expanding and contracting member, such as a spring, accordion, telescoping, or bellows-type device. In general, the shock absorbing member can be located such that relative movement between the sensor, the mounting unit, and the host is absorbed without (or minimally) affecting the connection of the sensor to the mounting unit and/or the sensor stability within the implantation site; for example, the shock-absorbing member can be formed as a part of or connected to the sensor 32.

FIGS. 12A to 12C are perspective and side views of a sensor system including the mounting unit 14 and electronics unit 16 attached thereto. After sensor insertion, the transcutaneous analyte sensor system 10 measures a concentration of an analyte or a substance indicative of the concentration or presence of the analyte as described above. Although the examples are directed to a glucose sensor, the analyte sensor can be a sensor capable of determining the level of any suitable analyte in the body, for example, oxygen, lactase, insulin, hormones, cholesterol, medicaments, viruses, or the like. Once the electronics unit 16 is connected to the mounting unit 14, the sensor 32 is able to measure levels of the analyte in the host.

Detachable connection between the mounting unit 14 and electronics unit 16 provides improved manufacturability, namely, the relatively inexpensive mounting unit 14 can be disposed of when replacing the sensor system after its usable life, while the relatively more expensive electronics unit 16 can be reusable with multiple sensor systems. In certain embodiments, the electronics unit 16 is configured with programming, for example, initialization, calibration reset, failure testing, or the like, each time it is initially inserted into the cavity and/or each time it initially communicates with the sensor 32. However, an integral (non-detachable) electronics unit can be configured as is appreciated by one skilled in the art.

Referring to the mechanical fit between the mounting unit 14 and the electronics unit 16 (and/or applicator 12), a variety of mechanical joints are contemplated, for example, snap fit, interference fit, or slide fit. In the illustrated embodiment of FIGS. 12A to 12C, tabs 120 are provided on the mounting unit 14 and/or electronics unit 16 that enable a secure connection therebetween. The tabs 120 of the illustrated embodiment can improve ease of mechanical connection by providing alignment of the mounting unit and electronics unit and additional rigid support for force and counter force by the user (e.g., fingers) during connection. However, other configurations with or without guiding tabs are contemplated, such as illustrated in FIGS. 10 and 11, for example.

In some circumstances, a drift of the sensor signal can cause inaccuracies in sensor performance and/or require re-calibration of the sensor. Accordingly, it can be advantageous to provide a sealant, whereby moisture (e.g. water and water vapor) cannot substantially penetrate to the sensor and its connection to the electrical contacts. The sealant described herein can be used alone or in combination with the sealing member 36 described in more detail above, to seal the sensor from moisture in the external environment.

Preferably, the sealant fills in holes, crevices, or other void spaces between the mounting unit 14 and electronics unit 16 and/or around the sensor 32 within the mounting unit 32. For example, the sealant can surround the sensor in the portion of the sensor 32 that extends through the contacts 28. Additionally, the sealant can be disposed within the additional void spaces, for example a hole 122 that extends through the sealing member 36.

Preferably, the sealant comprises a water impermeable material or compound, for example, oil, grease, or gel. In one exemplary embodiment, the sealant comprises petroleum jelly and is used to provide a moisture barrier surrounding the sensor 32. In one experiment, petroleum jelly was liquefied by heating, after which a sensor 32 was immersed into the liquefied petroleum jelly to coat the outer surfaces thereof. The sensor was then assembled into a housing and inserted into a host, during which deployment the sensor was inserted through the electrical contacts 28 and the petroleum jelly conforming therebetween. Sensors incorporating petroleum jelly, such as described above, when compared to sensors without the petroleum jelly moisture barrier exhibited less or no signal drift over time when studied in a humid or submersed environment. While not wishing to be bound by theory, it is believed that incorporation of a moisture barrier surrounding the sensor, especially between the sensor and its associated electrical contacts, reduces or eliminates the effects of humidity on the sensor signal. The viscosity of grease or oil-based moisture barriers allows penetration into and through even small cracks or crevices within the sensor and mounting unit, displacing moisture and thereby increasing the sealing properties thereof. U.S. Pat. No. 4,259,540 and U.S. Pat. No. 5,285,513 disclose materials suitable for use as a water impermeable material (sealant).

Referring to the electrical fit between the sensor 32 and the electronics unit 16, contacts 28 (through which the sensor extends) are configured to electrically connect with mutually engaging contacts on the electronics unit 16. A variety of configurations are contemplated; however, the mutually engaging contacts operatively connect upon detachable connection of the electronics unit 16 with the mounting unit 14, and are substantially sealed from external moisture by sealing member 36. Even with the sealing member, some circumstances can exist wherein moisture can penetrate into the area surrounding the sensor 32 and or contacts, for example, exposure to a humid or wet environment (e.g., caused by sweat, showering, or other environmental causes). It has been observed that exposure of the sensor to moisture can be a cause of baseline signal drift of the sensor over time. For example in a glucose sensor, the baseline is the component of a glucose sensor signal that is not related to glucose (the amount of signal if no glucose is present), which is ideally constant over time. However, some circumstances my exist wherein the baseline can fluctuate over time, also referred to as drift, which can be caused, for example, by changes in a host's metabolism, cellular migration surrounding the sensor, interfering species, humidity in the environment, and the like.

In some embodiments, the mounting unit is designed to provide ventilation (e.g., a vent hole 124) between the exit-site and the sensor. In certain embodiments, a filter (not shown) is provided in the vent hole 124 that allows the passage of air, while preventing contaminants from entering the vent hole 124 from the external environment. While not wishing to be bound by theory, it is believed that ventilation to the exit-site (or to the sensor 32) can reduce or eliminate trapped moisture or bacteria, which can otherwise increase the growth and/or lifetime of bacteria adjacent to the sensor.

In some alternative embodiments, a sealing material is provided, which seals the needle and/or sensor from contamination of the external environment during and after sensor insertion. For example, one problem encountered in conventional transcutaneous devices is infection of the exit-site of the wound. For example, bacteria or contaminants can migrate from ex vivo, for example, any ex vivo portion of the device or the ex vivo environment, through the exit-site of the needle/sensor, and into the subcutaneous tissue, causing contamination and infection. Bacteria and/or contaminants can originate from handling of the device, exposed skin areas, and/or leakage from the mounting unit (external to) on the host. In many conventional transcutaneous devices, there exists some path of migration for bacteria and contaminants to the exit-site, which can become contaminated during sensor insertion or subsequent handling or use of the device. Furthermore, in some embodiments of a transcutaneous analyte sensor, the insertion-aiding device (for example, needle) is an integral part of the mounting unit; namely, the device stores the insertion device after insertion of the sensor, which is isolated from the exit-site (namely, point-of-entry of the sensor) after insertion.

Accordingly, these alternative embodiments provide a sealing material on the mounting unit, interposed between the housing and the skin, wherein the needle and/or sensor are adapted to extend through, and be sealed by, the sealing material. The sealing material is preferably formed from a flexible material that substantially seals around the needle/sensor. Appropriate flexible materials include malleable materials, elastomers, gels, greases, or the like (e.g. see U.S. Pat. No. 4,259,540 and U.S. Pat. No. 5,285,513). However, not all embodiments include a sealing material, and in some embodiments a clearance hole or other space surrounding the needle and/or sensor is preferred.

In one embodiment, the base 24 of the mounting unit 14 is formed from a flexible material, for example silicone, which by its elastomeric properties seals the needle and/or sensor at the exit port 126, such as is illustrated in FIGS. 11A and 11B. Thus, sealing material can be formed as a unitary or integral piece with the back surface 25 of the mounting unit 14, or with an adhesive pad 8 on the back surface of the mounting unit, however alternatively can be a separate part secured to the device. In some embodiments, the sealing material can extend through the exit port 126 above or below the plane of the adhesive pad surface, or the exit port 126 can comprise a septum seal such as those used in the medical storage and disposal industries (for example, silica gel sandwiched between upper and lower seal layers, such as layers comprising chemically inert materials such as PTFE). A variety of known septum seals can be implemented into the exit port of the preferred embodiments described herein. Whether the sealing material is integral with or a separate part attached to the mounting unit 14, the exit port 126 is advantageously sealed so as to reduce or eliminate the migration of bacteria or other contaminants to or from the exit-site of the wound and/or within the mounting unit.

During use, a host or caretaker positions the mounting unit at the appropriate location on or near the host's skin and prepares for sensor insertion. During insertion, the needle aids in sensor insertion, after which the needle is retracted into the mounting unit leaving the sensor in the subcutaneous tissue. In this embodiment, the exit-port 126 includes a layer of sealing material, such as a silicone membrane, that encloses the exit-port in a configuration that protects the exit-site from contamination that can migrate from the mounting unit or spacing external to the exit-site. Thus, when the sensor 32 and/or needle 72 extend through, for example, an aperture or a puncture in the sealing material, to provide communication between the mounting unit and subcutaneous space, a seal is formed therebetween. Elastomeric sealing materials can be advantageous in some embodiments because the elasticity provides a conforming seal between the needle/sensor and the mounting unit and/or because the elasticity provides shock-absorbing qualities allowing relative movement between the device and the various layers of the host's tissue, for example.

In some alternative embodiments, the sealing material includes a bioactive agent incorporated therein. Suitable bioactive agents include those which are known to discourage or prevent bacteria and infection, for example, anti-inflammatory, antimicrobials, antibiotics, or the like. It is believed that diffusion or presence of a bioactive agent can aid in prevention or elimination of bacteria adjacent to the exit-site.

In practice, after the sensor 32 has been inserted into the host's tissue, and an electrical connection formed by mating the electronics unit 16 to the mounting unit 14, the sensor measures an analyte concentration continuously or continually, for example, at an interval of from about fractions of a second to about 10 minutes or more.

FIG. 13 is an exploded perspective view of one exemplary embodiment of a continuous glucose sensor 1310A. In this embodiment, the sensor is preferably wholly implanted into the subcutaneous tissue of a host, such as described in U.S. Patent Publication No. US-2006-0015020-A1; U.S. Patent Publication No. US-2005-0245799-A1; U.S. Patent Publication No. US-2005-0192557-A1; U.S. Pat. No. 7,134,999; U.S. Patent Publication No. US-2005-0027463-A1; and U.S. Pat. No. 6,001,067, each of which is incorporated herein by reference in their entirety. In this exemplary embodiment, a body 1320 and a sensing region 1321 house the electrodes 1322 and sensor electronics (see FIG. 14). The three electrodes 1322 are operably connected to the sensor electronics (see FIG. 14) and are covered by a sensing membrane 1323 and a biointerface membrane 1324, which are attached by a clip 1325.

In one embodiment, the three electrodes 1322 include a platinum working electrode, a platinum counter electrode, and a silver/silver chloride reference electrode. The top ends of the electrodes are in contact with an electrolyte phase (not shown), which is a free-flowing fluid phase disposed between the sensing membrane 1323 and the electrodes 1322. The sensing membrane 1323 includes an enzyme, for example, glucose oxidase, and covers the electrolyte phase. The biointerface membrane 1324 covers the sensing membrane 1323 and serves, at least in part, to protect the sensor 1310A from external forces that can result in environmental stress cracking of the sensing membrane 1323. U.S. Pat. No. 7,192,450 describes a biointerface membrane that can be used in conjunction with the preferred embodiments, and is incorporated herein by reference in its entirety.

In one embodiment, the biointerface membrane 1324 generally includes a cell disruptive domain most distal from the electrochemically reactive surfaces and a cell impermeable domain less distal from the electrochemically reactive surfaces than the cell disruptive domain. The cell disruptive domain is preferably designed to support tissue ingrowth, disrupt contractile forces typically found in a foreign body response, encourage vascularity within the membrane, and disrupt the formation of a barrier cell layer. The cell impermeable domain is preferably resistant to cellular attachment, impermeable to cells, and composed of a biostable material.

In one embodiment, the sensing membrane 1323 generally provides one or more of the following functions: 1) protection of the exposed electrode surface from the biological environment, 2) diffusion resistance (limitation) of the analyte, 3) a catalyst for enabling an enzymatic reaction, 4) limitation or blocking of interfering species, and 5) hydrophilicity at the electrochemically reactive surfaces of the sensor interface, such as described in U.S. Patent Publication No. 2005-0245799-A1, which is incorporated herein by reference in its entirety. Accordingly, the sensing membrane 1323 preferably includes a plurality of domains or layers, for example, an electrolyte domain, an interference domain, an enzyme domain (for example, glucose oxidase), a resistance domain, and can additionally include an oxygen domain (not shown), and/or a bioprotective domain (not shown), such as described in more detail herein and in the above-cited U.S. Patent Publication No. 2005-0245799-A1. However, it is understood that a sensing membrane modified for other devices, for example, by including fewer or additional domains is within the scope of the preferred embodiments.

In some embodiments, the domains of the biointerface and sensing membranes are formed from materials such as silicone, polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyurethanes, cellulosic polymers, polysulfones and block copolymers thereof including, for example, di-block, tri-block, alternating, random and graft copolymers. U.S. Patent Publication No. 2005-0245799-A1, which is incorporated herein by reference in its entirety, describes biointerface and sensing membrane configurations and materials that can be applied to the preferred embodiments.

In the illustrated embodiment, the counter electrode is provided to balance the current generated by the species being measured at the working electrode. In the case of a glucose oxidase based glucose sensor, the species being measured at the working electrode is H₂O₂. Glucose oxidase catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate according to the following reaction: Glucose+O₂→Gluconate+H₂O₂

The change in H₂O₂ can be monitored to determine glucose concentration because for each glucose molecule metabolized, there is a proportional change in the product H₂O₂. Oxidation of H₂O₂ by the working electrode is balanced by reduction of ambient oxygen, enzyme generated H₂O₂, or other reducible species at the counter electrode. The H₂O₂ produced from the glucose oxidase reaction further reacts at the surface of working electrode and produces two protons (2H⁺), two electrons (2e⁻), and one oxygen molecule (O₂).

In one embodiment, a potentiostat is employed to monitor the electrochemical reaction at the electrochemical cell. The potentiostat applies a constant potential to the working and reference electrodes to determine a current value. The current that is produced at the working electrode (and flows through the circuitry to the counter electrode) is substantially proportional to the amount of H₂O₂ that diffuses to the working electrode. Accordingly, a raw signal can be produced that is representative of the concentration of glucose in the user's body, and therefore can be utilized to estimate a meaningful glucose value, such as is described herein.

Sensor Electronics

The following description of sensor electronics associated with the electronics unit is applicable to a variety of continuous analyte sensors, such as non-invasive, minimally invasive, and/or invasive (e.g. transcutaneous and wholly implantable) sensors. For example, the sensor electronics and data processing as well as the receiver electronics and data processing described below can be incorporated into the wholly implantable glucose sensor disclosed in U.S. Patent Publication No. 2005-0245799-A1 and U.S. Patent Publication No. US-2006-0015020-A1.

FIG. 14 is a block diagram that illustrates the electronics 132 associated with the sensor system 10 in one embodiment. In this embodiment, a potentiostat 134 is shown, which is operably connected to an electrode system (such as described above) and provides a voltage to the electrodes, which biases the sensor to enable measurement of an current signal indicative of the analyte concentration in the host (also referred to as the analog portion). In some embodiments, the potentiostat includes a resistor (not shown) that translates the current into voltage. In some alternative embodiments, a current to frequency converter is provided that is configured to continuously integrate the measured current, for example, using a charge counting device.

An A/D converter 136 digitizes the analog signal into a digital signal, also referred to as “counts” for processing. Accordingly, the resulting raw data stream in counts, also referred to as raw sensor data, is directly related to the current measured by the potentiostat 134.

A processor module 138 includes the central control unit that controls the processing of the sensor electronics 132. In some embodiments, the processor module includes a microprocessor, however a computer system other than a microprocessor can be used to process data as described herein, for example an ASIC can be used for some or all of the sensor's central processing. The processor typically provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (for example, programming for data smoothing and/or replacement of signal artifacts such as is described in U.S. Patent No. US-2005-0043598-A1. The processor additionally can be used for the system's cache memory, for example for temporarily storing recent sensor data. In some embodiments, the processor module comprises memory storage components such as ROM, RAM, dynamic-RAM, static-RAM, non-static RAM, EEPROM, rewritable ROMs, flash memory, or the like.

In some embodiments, the processor module comprises a digital filter, for example, an infinite impulse response (IIR) or finite impulse response (FIR) filter, configured to smooth the raw data stream from the A/D converter. Generally, digital filters are programmed to filter data sampled at a predetermined time interval (also referred to as a sample rate). In some embodiments, wherein the potentiostat is configured to measure the analyte at discrete time intervals, these time intervals determine the sample rate of the digital filter. In some alternative embodiments, wherein the potentiostat is configured to continuously measure the analyte, for example, using a current-to-frequency converter as described above, the processor module can be programmed to request a digital value from the A/D converter at a predetermined time interval, also referred to as the acquisition time. In these alternative embodiments, the values obtained by the processor are advantageously averaged over the acquisition time due the continuity of the current measurement. Accordingly, the acquisition time determines the sample rate of the digital filter. In preferred embodiments, the processor module is configured with a programmable acquisition time, namely, the predetermined time interval for requesting the digital value from the A/D converter is programmable by a user within the digital circuitry of the processor module. An acquisition time of from about 2 seconds to about 512 seconds is preferred; however any acquisition time can be programmed into the processor module. A programmable acquisition time is advantageous in optimizing noise filtration, time lag, and processing/battery power.

Preferably, the processor module is configured to build the data packet for transmission to an outside source, for example, an RF transmission to a receiver as described in more detail below. Generally, the data packet comprises a plurality of bits that can include a sensor ID code, raw data, filtered data, and/or error detection or correction. The processor module can be configured to transmit any combination of raw and/or filtered data.

In some embodiments, the processor module further comprises a transmitter portion that determines the transmission interval of the sensor data to a receiver, or the like. In some embodiments, the transmitter portion, which determines the interval of transmission, is configured to be programmable. In one such embodiment, a coefficient can be chosen (e.g. a number of from about 1 to about 100, or more), wherein the coefficient is multiplied by the acquisition time (or sampling rate), such as described above, to define the transmission interval of the data packet. Thus, in some embodiments, the transmission interval is programmable between about 2 seconds and about 850 minutes, more preferably between about 30 second and 5 minutes; however, any transmission interval can be programmable or programmed into the processor module. However, a variety of alternative systems and methods for providing a programmable transmission interval can also be employed. By providing a programmable transmission interval, data transmission can be customized to meet a variety of design criteria (e.g. reduced battery consumption, timeliness of reporting sensor values, etc.)

Conventional glucose sensors measure current in the nanoAmp range. In contrast to conventional glucose sensors, the preferred embodiments are configured to measure the current flow in the picoAmp range, and in some embodiments, femtoAmps. Namely, for every unit (mg/dL) of glucose measured, at least one picoAmp of current is measured. Preferably, the analog portion of the A/D converter 136 is configured to continuously measure the current flowing at the working electrode and to convert the current measurement to digital values representative of the current. In one embodiment, the current flow is measured by a charge counting device (e.g. a capacitor). Thus, a signal is provided, whereby a high sensitivity maximizes the signal received by a minimal amount of measured hydrogen peroxide (e.g. minimal glucose requirements without sacrificing accuracy even in low glucose ranges), reducing the sensitivity to oxygen limitations in vivo (e.g. in oxygen-dependent glucose sensors).

A battery 144 is operably connected to the sensor electronics 132 and provides the power for the sensor. In one embodiment, the battery is a lithium manganese dioxide battery; however, any appropriately sized and powered battery can be used (for example, AAA, nickel-cadmium, zinc-carbon, alkaline, lithium, nickel-metal hydride, lithium-ion, zinc-air, zinc-mercury oxide, silver-zinc, and/or hermetically-sealed). In some embodiments, the battery is rechargeable, and/or a plurality of batteries can be used to power the system. The sensor can be transcutaneously powered via an inductive coupling, for example. In some embodiments, a quartz crystal 96 is operably connected to the processor 138 and maintains system time for the computer system as a whole, for example for the programmable acquisition time within the processor module.

Optional temperature probe 140 is shown, wherein the temperature probe is located on the electronics assembly or the glucose sensor itself. The temperature probe can be used to measure ambient temperature in the vicinity of the glucose sensor. This temperature measurement can be used to add temperature compensation to the calculated glucose value.

An RF module 148 is operably connected to the processor 138 and transmits the sensor data from the sensor to a receiver within a wireless transmission 150 via antenna 152. In some embodiments, a second quartz crystal 154 provides the time base for the RF carrier frequency used for data transmissions from the RF transceiver. In some alternative embodiments, however, other mechanisms, such as optical, infrared radiation (IR), ultrasonic, or the like, can be used to transmit and/or receive data.

In the RF telemetry module of the preferred embodiments, the hardware and software are designed for low power requirements to increase the longevity of the device (for example, to enable a life of from about 3 to about 24 months, or more) with maximum RF transmittance from the in vivo environment to the ex vivo environment for wholly implantable sensors (for example, a distance of from about one to ten meters or more). Preferably, a high frequency carrier signal of from about 402 MHz to about 433 MHz is employed in order to maintain lower power requirements. Additionally, in wholly implantable devices, the carrier frequency is adapted for physiological attenuation levels, which is accomplished by tuning the RF module in a simulated in vivo environment to ensure RF functionality after implantation; accordingly, the preferred glucose sensor can sustain sensor function for 3 months, 6 months, 12 months, or 24 months or more.

When a sensor is first implanted into host tissue, the sensor and receiver are initialized. This is referred to as start-up mode, and involves optionally resetting the sensor data and calibrating the sensor 32. In selected embodiments, mating the electronics unit 16 to the mounting unit triggers a start-up mode. In other embodiments, the start-up mode is triggered by the receiver, which is described in more detail with reference to FIG. 21, below.

Preferably, the electronics unit 16 indicates to the receiver (FIGS. 15 and 17) that calibration is to be initialized (or re-initialized). The electronics unit 16 transmits a series of bits within a transmitted data packet wherein a sensor code can be included in the periodic transmission of the device. The status code is used to communicate sensor status to the receiving device. The status code can be inserted into any location in the transmitted data packet, with or without other sensor information. In one embodiment, the status code is designed to be unique or near unique to an individual sensor, which can be accomplished using a value that increments, decrements, or changes in some way after the transmitter detects that a sensor has been removed and/or attached to the transmitter. In an alternative embodiment, the status code can be configured to follow a specific progression, such as a BCD interpretation of a Gray code.

In some embodiments, the sensor electronics 132 are configured to detect a current drop to zero in the working electrode 44 associated with removal of a sensor 32 from the host (or the electronics unit 16 from the mounting unit 14), which can be configured to trigger an increment of the status code. If the incremented value reaches a maximum, it can be designed to roll over to 0. In some embodiments, the sensor electronics are configured to detect a voltage change cycle associated with removal and/or re-insertion of the sensor, which can be sensed in the counter electrode (e.g. of a three-electrode sensor), which can be configured to trigger an increment of the status code.

In some embodiments, the sensor electronics 132 can be configured to send a special value (for example, 0) that indicates that the electronics unit is not attached when removal of the sensor (or electronics unit) is detected. This special value can be used to trigger a variety of events, for example, to halt display of analyte values. Incrementing or decrementing routines can be used to skip this special value.

In some embodiments, the electronics unit 16 is configured to include additional contacts, which are designed to sense a specific resistance, or passive value, in the sensor system while the electronics unit is attached to the mounting unit. Preferably, these additional contacts are configured to detect information about a sensor, for example, whether the sensor is operatively connected to the mounting unit, the sensor's ID, a calibration code, or the like. For example, subsequent to sensing the passive value, the sensor electronics can be configured to change the sensor ID code by either mapping the value to a specific code, or internally detecting that the code is different and adjusting the sensor ID code in a predictable manner. As another example, the passive value can include information on parameters specific to a sensor (such as in vitro sensitivity information as described elsewhere herein).

In some embodiments, the electronics unit 16 includes additional contacts configured to communicate with a chip disposed in the mounting unit 14. In this embodiment, the chip is designed with a unique or near-unique signature that can be detected by the electronics unit 16 and noted as different, and/or transmitted to the receiver 158 as the sensor ID code.

In some embodiments, the electronics unit 16 is inductively coupled to an RFID or similar chip in the mounting unit 14. In this embodiment, the RFID tag uniquely identifies the sensor 32 and allows the transmitter to adjust the sensor ID code accordingly and/or to transmit the unique identifier to the receiver 158.

In some situations, it can be desirable to wait an amount of time after insertion of the sensor to allow the sensor to equilibrate in vivo, also referred to as “break-in.” Accordingly, the sensor electronics can be configured to aid in decreasing the break-in time of the sensor by applying different voltage settings (for example, starting with a higher voltage setting and then reducing the voltage setting) to speed the equilibration process.

In some situations, the sensor may not properly deploy, connect to, or otherwise operate as intended. Accordingly, the sensor electronics can be configured such that if the current obtained from the working electrode, or the subsequent conversion of the current into digital counts, for example, is outside of an acceptable threshold, then the sensor is marked with an error flag, or the like. The error flag can be transmitted to the receiver to instruct the user to reinsert a new sensor, or to implement some other error correction.

The above-described detection and transmission methods can be advantageously employed to minimize or eliminate human interaction with the sensor, thereby minimizing human error and/or inconvenience. Additionally, the sensors of preferred embodiments do not require that the receiver be in proximity to the transmitter during sensor insertion. Any one or more of the above described methods of detecting and transmitting insertion of a sensor and/or electronics unit can be combined or modified, as is appreciated by one skilled in the art. F

Receiver

FIG. 15 is a perspective view of a sensor system, including wireless communication between a sensor and a receiver. Preferably the electronics unit 16 is wirelessly connected to a receiver 158 via one- or two-way RF transmissions or the like. However, a wired connection is also contemplated. The receiver 158 provides much of the processing and display of the sensor data, and can be selectively worn and/or removed at the host's convenience. Thus, the sensor system 10 can be discreetly worn, and the receiver 158, which provides much of the processing and display of the sensor data, can be selectively worn and/or removed at the host's convenience. Particularly, the receiver 158 includes programming for retrospectively and/or prospectively initiating a calibration, converting sensor data, updating the calibration, evaluating received reference and sensor data, and evaluating the calibration for the analyte sensor, such as described in more detail with reference to U.S. Patent Publication No. US-2005-0027463-A1.

FIGS. 16A to 16D are schematic views of a receiver in first, second, third, and fourth embodiments, respectively. A receiver 1640 comprises systems necessary to receive, process, and display sensor data from an analyte sensor, such as described elsewhere herein. Particularly, the receiver 1640 can be a pager-sized device, for example, and comprise a user interface that has a plurality of buttons 1642 and a liquid crystal display (LCD) screen 1644, and which can include a backlight. In some embodiments the user interface can also include a keyboard, a speaker, and a vibrator such as described with reference to FIG. 17A.

In some embodiments a user is able to toggle through some or all of the screens shown in FIGS. 16A to 16D using a toggle button on the receiver. In some embodiments, the user is able to interactively select the type of output displayed on their user interface. In some embodiments, the sensor output can have alternative configurations.

Receiver Electronics

FIG. 17A is a block diagram that illustrates the configuration of the medical device in one embodiment, including a continuous analyte sensor, a receiver, and an external device. In general, the analyte sensor system is any sensor configuration that provides an output signal indicative of a concentration of an analyte (e.g. invasive, minimally-invasive, and/or non-invasive sensors as described above). The output signal is sent to a receiver 158 and received by an input module 174, which is described in more detail below. The output signal is typically a raw data stream that is used to provide a useful value of the measured analyte concentration to a patient or a doctor, for example. In some embodiments, the raw data stream can be continuously or periodically algorithmically smoothed or otherwise modified to diminish outlying points that do not accurately represent the analyte concentration, for example due to signal noise or other signal artifacts, such as described in U.S. Pat. No. 6,931,327, which is incorporated herein by reference in its entirety.

Referring again to FIG. 17A, the receiver 158, which is operatively linked to the sensor system 10, receives a data stream from the sensor system 10 via the input module 174. In one embodiment, the input module includes a quartz crystal operably connected to an RF transceiver (not shown) that together function to receive and synchronize data streams from the sensor system 10. However, the input module 174 can be configured in any manner that is capable of receiving data from the sensor. Once received, the input module 174 sends the data stream to a processor 176 that processes the data stream, such as is described in more detail below.

The processor 176 is the central control unit that performs the processing, such as storing data, analyzing data streams, calibrating analyte sensor data, estimating analyte values, comparing estimated analyte values with time corresponding measured analyte values, analyzing a variation of estimated analyte values, downloading data, and controlling the user interface by providing analyte values, prompts, messages, warnings, alarms, or the like. The processor includes hardware and software that performs the processing described herein, for example flash memory provides permanent or semi-permanent storage of data, storing data such as sensor ID, receiver ID, and programming to process data streams (for example, programming for performing estimation and other algorithms described elsewhere herein) and random access memory (RAM) stores the system's cache memory and is helpful in data processing.

Preferably, the input module 174 or processor module 176 performs a Cyclic Redundancy Check (CRC) to verify data integrity, with or without a method of recovering the data if there is an error. In some embodiments, error correction techniques such as those that use Hamming codes or Reed-Solomon encoding/decoding methods are employed to correct for errors in the data stream. In one alternative embodiment, an iterative decoding technique is employed, wherein the decoding is processed iteratively (e.g. in a closed loop) to determine the most likely decoded signal. This type of decoding can allow for recovery of a signal that is as low as 0.5 dB above the noise floor, which is in contrast to conventional non-iterative decoding techniques (such as Reed-Solomon), which requires approximately 3 dB or about twice the signal power to recover the same signal (e.g. a turbo code).

An output module 178, which is integral with and/or operatively connected with the processor 176, includes programming for generating output based on the data stream received from the sensor system 10 and its processing incurred in the processor 176. In some embodiments, output is generated via a user interface 160.

The user interface 160 comprises a keyboard 162, speaker 164, vibrator 166, backlight 168, liquid crystal display (LCD) screen 170, and one or more buttons 172. The components that comprise the user interface 160 include controls to allow interaction of the user with the receiver. The keyboard 162 can allow, for example, input of user information about himself/herself, such as mealtime, exercise, insulin administration, customized therapy recommendations, and reference analyte values. The speaker 164 can produce, for example, audible signals or alerts for conditions such as present and/or estimated (e.g., predicted) hyperglycemic or hypoglycemic conditions in a person with diabetes. The vibrator 166 can provide, for example, tactile signals or alerts for reasons such as described with reference to the speaker, above. The backlight 168 can be provided, for example, to aid the user in reading the LCD 170 in low light conditions. The LCD 170 can be provided, for example, to provide the user with visual data output, such as is described in U.S. Patent Publication No. US-2005-0203360-A1. FIGS. 17B to 17D illustrate some additional visual displays that can be provided on the screen 170. In some embodiments, the LCD is a touch-activated screen, enabling each selection by a user, for example, from a menu on the screen. The buttons 172 can provide for toggle, menu selection, option selection, mode selection, and reset, for example. In some alternative embodiments, a microphone can be provided to allow for voice-activated control.

In some embodiments, prompts or messages can be displayed on the user interface to convey information to the user, such as reference outlier values, requests for reference analyte values, therapy recommendations, deviation of the measured analyte values from the estimated analyte values, or the like. Additionally, prompts can be displayed to guide the user through calibration or trouble-shooting of the calibration.

Additionally, data output from the output module 178 can provide wired or wireless, one- or two-way communication between the receiver 158 and an external device 180. The external device 180 can be any device that wherein interfaces or communicates with the receiver 158. In some embodiments, the external device 180 is a computer, and the receiver 158 is able to download historical data for retrospective analysis by the patient or physician, for example. In some embodiments, the external device 180 is a modem or other telecommunications station, and the receiver 158 is able to send alerts, warnings, emergency messages, or the like, via telecommunication lines to another party, such as a doctor or family member. In some embodiments, the external device 180 is an insulin pen, and the receiver 158 is able to communicate therapy recommendations, such as insulin amount and time to the insulin pen. In some embodiments, the external device 180 is an insulin pump, and the receiver 158 is able to communicate therapy recommendations, such as insulin amount and time to the insulin pump. The external device 180 can include other technology or medical devices, for example pacemakers, implanted analyte sensor patches, other infusion devices, telemetry devices, or the like. Some additional examples of external devices are described in more detail with reference to FIGS. 53-56.

The user interface 160, including keyboard 162, buttons 172, a microphone (not shown), and the external device 180, can be configured to allow input of data. Data input can be helpful in obtaining information about the patient (for example, meal time, exercise, or the like), receiving instructions from a physician (for example, customized therapy recommendations, targets, or the like), and downloading software updates, for example. Keyboard, buttons, touch-screen, and microphone are all examples of mechanisms by which a user can input data directly into the receiver. A server, personal computer, personal digital assistant, insulin pump, and insulin pen are examples of external devices that can provide useful information to the receiver. Other devices internal or external to the sensor that measure other aspects of a patient's body (for example, temperature sensor, accelerometer, heart rate monitor, oxygen monitor, or the like) can be used to provide input helpful in data processing. In one embodiment, the user interface can prompt the patient to select an activity most closely related to their present activity, which can be helpful in linking to an individual's physiological patterns, or other data processing. In another embodiment, a temperature sensor and/or heart rate monitor can provide information helpful in linking activity, metabolism, and glucose excursions of an individual. While a few examples of data input have been provided here, a variety of information can be input, which can be helpful in data processing.

FIG. 17B is an illustration of an LCD screen 170 showing continuous and single point glucose information in the form of a trend graph 184 and a single numerical value 186. The trend graph shows upper and lower boundaries 182 representing a target range between which the host should maintain his/her glucose values. Preferably, the receiver is configured such that these boundaries 182 can be configured or customized by a user, such as the host or a care provider. By providing visual boundaries 182, in combination with continuous analyte values over time (e.g., a trend graph 184), a user can better learn how to control his/her analyte concentration (e.g., a person with diabetes can better learn how to control his/her glucose concentration) as compared to single point (single numerical value 186) alone. Although FIG. 17B illustrates a 1-hour trend graph (e.g., depicted with a time range 188 of 1-hour), a variety of time ranges can be represented on the screen 170, for example, 3-hour, 9-hour, 1-day, and the like.

FIG. 17C is an illustration of an LCD screen 170 showing a low alert screen that can be displayed responsive to a host's analyte concentration falling below a lower boundary (see boundaries 182). In this exemplary screen, a host's glucose concentration has fallen to 55 mg/dL, which is below the lower boundary set in FIG. 17B, for example. The arrow 190 represents the direction of the analyte trend, for example, indicating that the glucose concentration is continuing to drop. The annotation 192 (“LOW”) is helpful in immediately and clearly alerting the host that his/her glucose concentration has dropped below a preset limit, and what may be considered to be a clinically safe value, for example. FIG. 17D is an illustration of an LCD screen 170 showing a high alert screen that can be displayed responsive to a host's analyte concentration rising above an upper boundary (see boundaries 182). In this exemplary screen, a host's glucose concentration has risen to 200 mg/dL, which is above a boundary set by the host, thereby triggering the high alert screen. The arrow 190 represents the direction of the analyte trend, for example, indicating that the glucose concentration is continuing to rise. The annotation 192 (“HIGH”) is helpful in immediately and clearly alerting the host that his/her glucose concentration has above a preset limit, and what may be considered to be a clinically safe value, for example.

Although a few exemplary screens are depicted herein, a variety of screens can be provided for illustrating any of the information described in the preferred embodiments, as well as additional information. A user can toggle between these screens (e.g., using buttons 172) and/or the screens can be automatically displayed responsive to programming within the receiver 158, and can be simultaneously accompanied by another type of alert (audible or tactile, for example).

In some embodiments the receiver 158 can have a length of from about 8 cm to about 15 cm, a width of from about 3.5 cm to about 10 cm, and/or a thickness of from about 1 cm to about 3.5 cm. In some embodiments the receiver 158 can have a volume of from about 120 cm³ to about 180 cm³, and can have a weight of from about 70 g to 130 g. The dimensions and volume can be higher or lower, depending, e.g., on the type of devices integrated (e.g., finger stick devices, pumps, PDAs, and the like.), the type of user interface employed, and the like.

In some embodiments, the receiver 158 is an application-specific device. In some embodiments the receiver 158 can be a device used for other functions, such as are described in U.S. Pat. No. 6,558,320. For example, the receiver 158 can be integrated into a personal computer (PC), a personal digital assistant (PDA), a cell phone, or another fixed or portable computing device. The integration of the receiver 158 function into a more general purpose device can comprise the addition of software and/or hardware to the device. Communication between the sensor electronics 16 and the receiver 158 function of the more general purpose device can be implemented with wired or wireless technologies. For example, a PDA can be configured with a data communications port and/or a wireless receiver. After the user establishes a communication link between the electronics unit 16 and the PDA, the electronics unit 16 transmits data to the PDA which then processes the data according to software which has been loaded thereon so as to display.

Algorithms

FIG. 18A provides a flow chart 200 that illustrates the initial calibration and data output of the sensor data in one embodiment, wherein calibration is responsive to reference analyte data. Initial calibration, also referred to as start-up mode, occurs at the initialization of a sensor, for example, the first time an electronics unit is used with a particular sensor. In certain embodiments, start-up calibration is triggered when the system determines that it can no longer remain in normal or suspended mode, which is described in more detail with reference to FIG. 21.

Calibration of an analyte sensor comprises data processing that converts sensor data signal into an estimated analyte measurement that is meaningful to a user. Accordingly, a reference analyte value is used to calibrate the data signal from the analyte sensor.

At block 202, a sensor data receiving module, also referred to as the sensor data module, receives sensor data (e.g. a data stream), including one or more time-spaced sensor data points, from the sensor 32 via the receiver 158, which can be in wired or wireless communication with the sensor 32. The sensor data point(s) can be smoothed (filtered) in certain embodiments using a filter, for example, a finite impulse response (FIR) or infinite impulse response (IIR) filter. During the initialization of the sensor, prior to initial calibration, the receiver receives and stores the sensor data, however it can be configured to not display any data to the user until initial calibration and, optionally, stabilization of the sensor has been established. In some embodiments, the data stream can be evaluated to determine sensor break-in (equilibration of the sensor in vitro or in vivo).

At block 204, a reference data receiving module, also referred to as the reference input module, receives reference data from a reference analyte monitor, including one or more reference data points. In one embodiment, the reference analyte points can comprise results from a self-monitored blood analyte test (e.g., finger stick test). For example, the user can administer a self-monitored blood analyte test to obtain an analyte value (e.g., point) using any known analyte sensor, and then enter the numeric analyte value into the computer system. Alternatively, a self-monitored blood analyte test is transferred into the computer system through a wired or wireless connection to the receiver (e.g. computer system) so that the user simply initiates a connection between the two devices, and the reference analyte data is passed or downloaded between the self-monitored blood analyte test and the receiver.

In yet another embodiment, the self-monitored analyte monitor (e.g., SMBG) is integral with the receiver so that the user simply provides a blood sample to the receiver, and the receiver runs the analyte test to determine a reference analyte value, such as is described in more detail herein and with reference to U.S. Patent Publication No. US-2005-0154271-A1, which is incorporated herein by reference in its entirety and which describes some systems and methods for integrating a reference analyte monitor into a receiver for a continuous analyte sensor.

In some embodiments, the integrated receiver comprises a microprocessor which can be programmed to process sensor data to perform the calibration. Such programming, which can be stored in a computer readable memory, can also comprise data acceptability testing using criteria such as that discussed above with reference to FIG. 18A. For example the microprocessor can be programmed so as to determine the rate of change of glucose concentration based on the continuous sensor data, and perform calibration only if the rate of change is below a predetermined threshold, such as 2 mg/dL/min. In some embodiments the receiver can also comprise modules to perform a calibration procedure such as is described herein. Such modules include, but are not limited to an input module, a data matching module, a calibration module, a conversion function module, a sensor data transformation module, a calibration evaluation module, a clinical module, a stability module, and a user interface, each of which have been described herein.

The monitor can be of any suitable configuration. For example, in one embodiment, the reference analyte points can comprise results from a self-monitored blood analyte test (e.g., from a finger stick test), such as those described in U.S. Pat. Nos. 6,045,567; 6,156,051; 6,197,040; 6,284,125; 6,413,410; and 6,733,655. In one such embodiment, the user can administer a self-monitored blood analyte test to obtain an analyte value (e.g., point) using any suitable analyte sensor, and then enter the numeric analyte value into the computer system (e.g., the receiver). In another such embodiment, a self-monitored blood analyte test comprises a wired or wireless connection to the receiver (e.g. computer system) so that the user simply initiates a connection between the two devices, and the reference analyte data is passed or downloaded between the self-monitored blood analyte test and the receiver. In yet another such embodiment, the self-monitored analyte test is integral with the receiver so that the user simply provides a blood sample to the receiver, and the receiver runs the analyte test to determine a reference analyte value.

Other suitable monitor configurations include, for example, those described in U.S. Pat. Nos. 4,994,167, 4,757,022, 6,551,494. In alternative embodiments, the single point glucose monitor of this particular embodiment can be configured as described with reference to U.S. Patent Publication No. US-2005-0154271-A1. In other alternative embodiments, the monitor can be configured using other glucose meter configurations. Numerous advantages associated with the integrated receiver, such as ensuring accurate time stamping of the single point glucose test at the receiver and other advantages described herein, can be provided by an integrated continuous glucose receiver and single point glucose monitor, such as described herein.

FIGS. 18B to 18F illustrate another embodiment of an integrated receiver, wherein a single point glucose monitor includes a stylus movably mounted to the integrated receiver for measurement of glucose in a biological sample. FIG. 18B is a perspective view of the integrated receiver housing in another embodiment, showing a single point glucose monitor including a stylus movably mounted to the integrated receiver, wherein the stylus is shown in a storage position. FIG. 18C is a perspective view of the integrated housing of FIG. 18B, showing the stylus in a testing position. FIG. 18D is a perspective view of a portion of the stylus of FIG. 18B, showing the sensing region. FIG. 18E is a perspective view of the integrated receiver housing of FIG. 18B, showing the stylus loaded with a disposable film, and in its testing position. FIG. 18F is a perspective view of a portion of the stylus of FIG. 18B, showing the sensing region with a disposable film stretched and/or disposed thereon.

In this embodiment, the integrated receiver provides 1892 a housing that integrates a single point glucose monitor 1894 and electronics (see FIG. 8) useful to receive, process, and display data on the user interface 1896. The single point glucose monitor 1894 permits rapid and accurate measurement of the amount of a particular substance (for example, glucose) in a biological fluid. Generally, the integrated receiver electronics process single point glucose monitor data, receive and process continuous glucose sensor data, including calibration of the continuous sensor data using the single point monitor data for example, and output data via the user interface 1896, such as is described below in more detail with reference to FIG. 8.

The single point glucose monitor 1894 includes a stylus 1898 that is movably mounted to the integrated receiver housing 1892 via a connector 1893. The connector 1893 can be a cord, bar, hinge, or any such connection means that allows the stylus to move from a first (storage) position (FIG. 18B) to a second (testing) position (FIG. 18C) on the housing. The stylus is not constrained to the first and second positions; rather the stylus can be configured to swing at various angles, about various pivots, or in any manner allowed by the connector for convenience to the user. In some alternative embodiments, the stylus 1898 is removably mounted on the integrated receiver housing 1892 and an operable connection can be established using a wireless connection, or alternatively using electrical contacts that operably connect the stylus 1898 that is removably mounted onto the integrated receiver housing 1892.

The stylus 1898 includes a sensing region 18100 on one end that is operably connected to the integrated receiver's electronics (FIG. 8). As illustrated in FIG. 18D, the sensing region 18100 is provided with at least two, preferably three electrodes 18102. In some embodiments a sensing membrane (not shown) is also disposed over the electrodes 18102 and/or the entire sensing region 18100. The sensing region includes the electrodes 18102 and the sensing membrane, which are configured to measure glucose in a manner such as described above with reference to the sensing region of FIGS. 2 and 4. In one embodiment, the sensing membrane is reusable and can be held on the sensing region 18100 by a clip, such as described with reference to FIG. 2. In alternative embodiments, the sensing membrane is reusable and can be disposed onto the sensing region using depositing or bonding techniques known in the art of polymers. In some embodiments the sensing membrane can be disposable or suitable for a single use.

In some embodiments, so as to maintain a preferred moisture condition of the sensing region 18100, and particularly of the sensing membrane, the integrated receiver housing 1892 includes a moisturizing solution chamber (not shown) located at the end of the receiving chamber 18104 that receives the stylus for storage, such that when the stylus is in its storage position (FIG. 18B), the sensing membrane is maintained in the moisturizing solution. A moisturizing solution port 18106 is in communication with the moisturizing solution chamber and allows for refilling of the moisturizing solution chamber using a moisturizing refill solution 18108. In some embodiments the moisturizing solution can be a sterile solution.

In some embodiments, additional to or alternative to maintaining the sensing region 18100 in a moisturizing solution chamber, a moisturizing solution can be applied to the sensing region 18100 at or around the time of sensing. For example, a user can apply a moisturizing solution to the sensing region 18100 just before applying the sensing region 18100 to an area of skin. Also, the user can apply the moisturizing solution to the area of skin.

When a biological sample 18106 (FIG. 18F) is placed on a surface, such as the surface of the sensing membrane and/or sensing region 18100, there contamination of the surface after use of the biological sample 18106 can be a concern. Accordingly, in some embodiments, a single-use disposable bioprotective film 18109 can be placed over the sensing region 18100 to provide protection from contamination. The bioprotective film 18109 can be any film with that allows the passage of glucose, but blocks the passage of undesired species in the blood that could damage or contaminate the sensing membrane and/or cause inaccurate measurements (for example, a thin film of very low molecular weight cutoff to prevent the transport of proteins, viruses, and the like). In some embodiments the bioprotective film 18109 is not single-use disposable, but can be treated and reused.

In some alternative embodiments, the bioprotective film 18109 further comprises a sensing membrane formed as a part of the film (for example, laminated to the film), instead of (or in addition to) a sensing membrane disposed on the sensing region. This alternative embodiment is particularly advantageous in that it provides a disposable sensing membrane that requires no cleaning step, for example.

Because the stylus 1898 can be put into direct contact with the biological sample 18106 (for example, on a finger or arm), no transfer mechanism is required, and therefore the sample size can be smaller than conventionally required. Additionally, sensing region 18100 may not require a separate cleaning step, because the bioprotective film 18109 fully protects the sensing region 18100 from contamination.

The integrated receiver 1892 housing further allows for storage and dispensing of the disposable films 18109. A shuttle mechanism 18110 is provided that preferably feeds the films 18109 into a spring-loaded storage chamber (not shown) beneath the shuttle mechanism 18110, or the like. The shuttle mechanism 18110 can be used to load the disposable films 18109, one at a time, into a dispensing chamber 18111 for dispensing onto the sensing region. In alternative embodiments, other storage and dispensing mechanisms can be configured as a part of the integrated receiver housing 1812 or separate therefrom.

In practice, the stylus 1898 is held in its storage position within the receiving chamber 18104 where it is protected and maintained with a preferred moisture condition (FIG. 18B). A user then withdrawals the stylus 1898 from the receiving chamber 18104 (FIG. 18C) and loads a disposable film 18109 by sliding the shuttle mechanism 18110 toward the dispensing chamber 18111. When the sensing region 18100 of the stylus 1898 presses on the disposable film 18109 within the dispensing chamber, the film will be stretched over and/or otherwise stick to the moist sensing membrane on the surface of the sensing region 18100 (FIG. 18E). At this point, the stylus 1898 is ready for a biological sample (for example, a blood sample) 18106. The stylus 1898 can be brought into contact with the finger or arm of the user to directly receive the biological sample from the user without the need for a transfer mechanism (FIG. 18F). After the test, the bioprotective film 18109 is removed from the sensing region and the stylus 1898 is replaced into the receiving chamber 18104 of the integrated receiver 1892.

In some alternative embodiments, the reference data is based on sensor data from another substantially continuous analyte sensor, e.g. a transcutaneous analyte sensor described herein, or another type of suitable continuous analyte sensor. In an embodiment employing a series of two or more transcutaneous (or other continuous) sensors, the sensors can be employed so that they provide sensor data in discrete or overlapping periods. In such embodiments, the sensor data from one continuous sensor can be used to calibrate another continuous sensor, or be used to confirm the validity of a subsequently employed continuous sensor.

In some embodiments, reference data can be subjected to “outlier detection” wherein the accuracy of a received reference analyte data is evaluated as compared to time-corresponding sensor data. In one embodiment, the reference data is compared to the sensor data on a modified Clarke Error Grid (e.g. a test similar to the Clarke Error Grid except the boundaries between the different regions are modified slightly) to determine if the data falls within a predetermined threshold. If the data is not within the predetermined threshold, then the receiver can be configured to request additional reference analyte data. If the additional reference analyte data confirms (e.g. closely correlates to) the first reference analyte data, then the first and second reference values are assumed to be accurate and calibration of the sensor is adjusted or re-initialized. Alternatively, if the second reference analyte value falls within the predetermined threshold, then the first reference analyte value is assumed to be an outlier and the second reference analyte value is used by the algorithm(s) instead. In one alternative embodiments of outlier detection, projection is used to estimate an expected analyte value, which is compared with the actual value and a delta evaluated for substantial correspondence. However, other methods of outlier detection are possible.

Certain acceptability parameters can be set for reference values received from the user. In some embodiments, the calibration process monitors the continuous analyte sensor data stream to determine a preferred time for capturing reference analyte concentration values for calibration of the continuous sensor data stream. In an example wherein the analyte sensor is a continuous glucose sensor, when data (for example, observed from the data stream) changes too rapidly, the reference glucose value may not be sufficiently reliable for calibration due to unstable glucose changes in the host. In contrast, when sensor glucose data are relatively stable (for example, relatively low rate of change), a reference glucose value can be taken for a reliable calibration. For example, in one embodiment, the receiver can be configured to only accept reference analyte values of from about 40 mg/dL to about 400 mg/dL. As another example, the receiver can be configured to only accept reference analyte values when the rate of change is less than a predetermined maximum, such as 1, 1.5, 2, 2.5, 3, or 3.5, mg/dL/min. As yet another example, the receiver can be configured to only accept reference analyte values when the rate of acceleration (or deceleration) is less than a predetermined maximum, such as 0.01 mg/dL/min², 0.02 mg/dL/min², 0.03 mg/dL/min², 0.04 mg/dL/min², or 0.05 mg/dL/min² or more.

In some embodiments, the reference data is pre-screened according to environmental and/or physiological issues, such as time of day, oxygen concentration, postural effects, and patient-entered environmental data. In one example embodiment, wherein the sensor comprises an implantable glucose sensor, an oxygen sensor within the glucose sensor is used to determine if sufficient oxygen is being provided to successfully complete the necessary enzyme and electrochemical reactions for glucose sensing. In another example wherein the sensor comprises an implantable glucose sensor, the counter electrode could be monitored for a “rail-effect,” that is, when insufficient oxygen is provided at the counter electrode causing the counter electrode to reach operational (e.g., circuitry) limits. In some embodiments the receiver is configured such that when conditions for accepting reference analyte values are not met, the user is notified. Such notice can include an indication as to the cause of the unacceptability, such as low oxygen or high rate of analyte value change. In some embodiments the indication can also include an indication of suggested corrective action, such as moderately increasing muscular activity so as to increase oxygen levels or to wait until the rate of analyte value change reduces to an acceptable value.

In one embodiment, the calibration process can prompt the user via the user interface to “calibrate now” when the reference analyte values are considered acceptable. In some embodiments, the calibration process can prompt the user via the user interface to obtain a reference analyte value for calibration at intervals, for example when analyte concentrations are at high and/or low values. In some additional embodiments, the user interface can prompt the user to obtain a reference analyte value for calibration based at least in part upon certain events, such as meals, exercise, large excursions in analyte levels, faulty or interrupted data readings, or the like. In some embodiments, the algorithms can provide information useful in determining when to request a reference analyte value. For example, when analyte values indicate approaching clinical risk, the user interface can prompt the user to obtain a reference analyte value.

In yet another example embodiment, the patient is prompted to enter data into the user interface, such as meal times and/or amount of exercise, which can be used to determine likelihood of acceptable reference data. Evaluation data, such as described in the paragraphs above, can be used to evaluate an optimum time for reference analyte measurement. Correspondingly, the user interface can then prompt the user to provide a reference data point for calibration within a given time period. Consequently, because the receiver proactively prompts the user during optimum calibration times, the likelihood of error due to environmental and physiological limitations may decrease and consistency and acceptability of the calibration may increase.

In some embodiments, the calibration process monitors the continuous analyte sensor data stream to determine a preferred time for capturing reference analyte concentration values for calibration of the continuous sensor data stream. In an example wherein the analyte sensor is a continuous glucose sensor, when data (for example, observed from the data stream) changes too rapidly, the reference glucose value may not be sufficiently reliable for calibration due to unstable glucose changes in the host. In contrast, when sensor glucose data are relatively stable (for example, relatively low rate of change), a reference glucose value can be taken for a reliable calibration. In one embodiment, the calibration process can prompt the user via the user interface to “calibrate now” when the analyte sensor is considered stable.

At block 206, a data matching module, also referred to as the processor module, matches reference data (e.g. one or more reference analyte data points) with substantially time corresponding sensor data (e.g. one or more sensor data points) to provide one or more matched data pairs. One reference data point can be matched to one time corresponding sensor data point to form a matched data pair. Alternatively, a plurality of reference data points can be averaged (e.g. equally or non-equally weighted average, mean-value, median, or the like) and matched to one time corresponding sensor data point to form a matched data pair, one reference data point can be matched to a plurality of time corresponding sensor data points averaged to form a matched data pair, or a plurality of reference data points can be averaged and matched to a plurality of time corresponding sensor data points averaged to form a matched data pair.

In one embodiment, time corresponding sensor data comprises one or more sensor data points that occur from about 0 minutes to about 20 minutes after the reference analyte data time stamp (e.g. the time that the reference analyte data is obtained). In one embodiment, a 5-minute time delay is chosen to compensate for a system time-lag (e.g. the time necessary for the analyte to diffusion through a membrane(s) of an analyte sensor). In alternative embodiments, the time corresponding sensor value can be greater than or less than that of the above-described embodiment, for example ±60 minutes. Variability in time correspondence of sensor and reference data can be attributed to, for example, a longer or shorter time delay introduced by the data smoothing filter, or if the configuration of the analyte sensor incurs a greater or lesser physiological time lag.

In some implementations of the sensor, the reference analyte data is obtained at a time that is different from the time that the data is input into the receiver. Accordingly, the “time stamp” of the reference analyte (e.g., the time at which the reference analyte value was obtained) is not the same as the time at which the receiver obtained the reference analyte data. Therefore, some embodiments include a time stamp requirement that ensures that the receiver stores the accurate time stamp for each reference analyte value, that is, the time at which the reference value was actually obtained from the user.

In certain embodiments, tests are used to evaluate the best-matched pair using a reference data point against individual sensor values over a predetermined time period (e.g. about 30 minutes). In one such embodiment, the reference data point is matched with sensor data points at 5-minute intervals and each matched pair is evaluated. The matched pair with the best correlation can be selected as the matched pair for data processing. In some alternative embodiments, matching a reference data point with an average of a plurality of sensor data points over a predetermined time period can be used to form a matched pair.

In certain embodiments, the data matching module only forms matched pairs when a certain analyte value condition is satisfied. Such a condition can include any of the conditions discussed above with reference to embodiments pre-screening or conditionally accepting reference analyte value data at block 204.

At block 208, a calibration set module, also referred to as the calibration module or processor module, forms an initial calibration set from a set of one or more matched data pairs, which are used to determine the relationship between the reference analyte data and the sensor analyte data. The matched data pairs, which make up the initial calibration set, can be selected according to predetermined criteria. The criteria for the initial calibration set can be the same as, or different from, the criteria for the updated calibration sets. In certain embodiments, the number (n) of data pair(s) selected for the initial calibration set is one. In other embodiments, n data pairs are selected for the initial calibration set wherein n is a function of the frequency of the received reference data points. In various embodiments, two data pairs make up the initial calibration set or six data pairs make up the initial calibration set. In an embodiment wherein a substantially continuous analyte sensor provides reference data, numerous data points are used to provide reference data from more than 6 data pairs (e.g. dozens or even hundreds of data pairs). In one exemplary embodiment, a substantially continuous analyte sensor provides 288 reference data points per day (every five minutes for twenty-four hours), thereby providing an opportunity for a matched data pair 288 times per day, for example. While specific numbers of matched data pairs are referred to in the preferred embodiments, any suitable number of matched data pairs per a given time period can be employed.

In certain embodiments, the data pairs are selected only when a certain analyte value condition is satisfied. Such a condition can include any of the conditions discussed above with reference to embodiments pre-screening or conditionally accepting reference analyte value data at block 204. In certain embodiments, the data pairs that form the initial calibration set are selected according to their time stamp, for example, by waiting a predetermined “break-in” time period after implantation, the stability of the sensor data can be increased. In certain embodiments, the data pairs that form the initial calibration set are spread out over a predetermined time period, for example, a period of two hours or more. In certain embodiments, the data pairs that form the initial calibration set are spread out over a predetermined glucose range, for example, spread out over a range of at least 90 mg/dL or more.

At block 210, a conversion function module, also referred to as the conversion module or processor module, uses the calibration set to create a conversion function. The conversion function substantially defines the relationship between the reference analyte data and the analyte sensor data.

A variety of known methods can be used with the preferred embodiments to create the conversion function from the calibration set. In one embodiment, wherein a plurality of matched data points form the calibration set, a linear least squares regression is used to calculate the conversion function; for example, this regression calculates a slope and an offset using the equation y=mx+b. A variety of regression or other conversion schemes can be implemented herein.

In certain embodiments, the conversion function module only creates a conversion function when a certain analyte value condition is satisfied. Such a condition can include any of the conditions discussed above with reference to embodiments pre-screening or conditionally accepting reference analyte value data at block 204 or with reference to selecting data pairs at block 208.

In some alternative embodiments, the sensor is calibrated with a single-point through the use of a dual-electrode system to simplify sensor calibration. In one such dual-electrode system, a first electrode functions as a hydrogen peroxide sensor including a membrane system containing glucose-oxidase disposed thereon, which operates as described herein. A second electrode is a hydrogen peroxide sensor that is configured similar to the first electrode, but with a modified membrane system (with the enzyme domain removed, for example). This second electrode provides a signal composed mostly of the baseline signal, b.

In some dual-electrode systems, the baseline signal is (electronically or digitally) subtracted from the glucose signal to obtain a glucose signal substantially without baseline. Accordingly, calibration of the resultant difference signal can be performed by solving the equation y=mx with a single paired measurement. Calibration of the implanted sensor in this alternative embodiment can be made less dependent on the values/range of the paired measurements, less sensitive to error in manual blood glucose measurements, and can facilitate the sensor's use as a primary source of glucose information for the user. U.S. Patent Publication No. US-2005-0143635-A1 describes systems and methods for subtracting the baseline from a sensor signal.

In some alternative dual-electrode system embodiments, the analyte sensor is configured to transmit signals obtained from each electrode separately (e.g., without subtraction of the baseline signal). In this way, the receiver can process these signals to determine additional information about the sensor and/or analyte concentration. For example, by comparing the signals from the first and second electrodes, changes in baseline and/or sensitivity can be detected and/or measured and used to update calibration (e.g., without the use of a reference analyte value). In one such example, by monitoring the corresponding first and second signals over time, an amount of signal contributed by baseline can be measured. In another such example, by comparing fluctuations in the correlating signals over time, changes in sensitivity can be detected and/or measured.

In some alternative embodiments, a regression equation y=mx+b is used to calculate the conversion function; however, prior information can be provided for m and/or b, thereby enabling calibration to occur with fewer paired measurements. In one calibration technique, prior information (e.g., obtained from in vivo or in vitro tests) determines a sensitivity of the sensor and/or the baseline signal of the sensor by analyzing sensor data from measurements taken by the sensor (e.g., prior to inserting the sensor). For example, if there exists a predictive relationship between in vitro sensor parameters and in vivo parameters, then this information can be used by the calibration procedure. For example, if a predictive relationship exists between in vitro sensitivity and in vivo sensitivity, m≈f(m_(invitro)) then the predicted m can be used, along with a single matched pair, to solve for b (b=y−mx). If, in addition, b can be assumed=0, for example with a dual-electrode configuration that enables subtraction of the baseline from the signal such as described above, then both m and b are known a priori, matched pairs are not needed for calibration, and the sensor can be completely calibrated e.g. without the need for reference analyte values (e.g. values obtained after implantation in vivo.)

In another alternative embodiment, prior information can be provided to guide or validate the baseline (b) and/or sensitivity (m) determined from the regression analysis. In this embodiment, boundaries can be set for the regression line that defines the conversion function such that working sensors are calibrated accurately and easily (with two points), and non-working sensors are prevented from being calibrated. If the boundaries are drawn too tightly, a working sensor may not enter into calibration. Likewise, if the boundaries are drawn too loosely, the scheme can result in inaccurate calibration or can permit non-working sensors to enter into calibration. For example, subsequent to performing regression, the resulting slope and/or baseline are tested to determine whether they fall within a predetermined acceptable threshold (boundaries). These predetermined acceptable boundaries can be obtained from in vivo or in vitro tests (e.g. by a retrospective analysis of sensor sensitivities and/or baselines collected from a set of sensors/patients, assuming that the set is representative of future data).

If the slope and/or baseline fall within the predetermined acceptable boundaries, then the regression is considered acceptable and processing continues to the next step (e.g. block 212). Alternatively, if the slope and/or baseline fall outside the predetermined acceptable boundaries, steps can be taken to either correct the regression or fail-safe such that a system will not process or display errant data. This can be useful in situations wherein regression results in errant slope or baseline values. For example, when points (matched pairs) used for regression are too close in value, the resulting regression statistically is less accurate than when the values are spread farther apart. As another example, a sensor that is not properly deployed or is damaged during deployment can yield a skewed or errant baseline signal.

In some alternative embodiments, the sensor system does not require initial and/or update calibration by the host; in these alternative embodiments, also referred to as “zero-point calibration” embodiments, use of the sensor system without requiring a reference analyte measurement for initial and/or update calibration is enabled. In general, the systems and methods of the preferred embodiments provide for stable and repeatable sensor manufacture, particularly when tightly controlled manufacturing processes are utilized. Namely, a batch of sensors of the preferred embodiments can be designed with substantially the same baseline (b) and/or sensitivity (m) (+/−10%) when tested in vitro. Additionally, the sensor of the preferred embodiments can be designed for repeatable m and b in vivo. Thus, an initial calibration factor (conversion function) can be programmed into the sensor (sensor electronics and/or receiver electronics) that enables conversion of raw sensor data into calibrated sensor data solely using information obtained prior to implantation (namely, initial calibration does not require a reference analyte value). Additionally, to obviate the need for recalibration (update calibration) during the life of the sensor, the sensor is designed to minimize drift of the sensitivity and/or baseline over time in vivo. Accordingly, the preferred embodiments can be manufactured for zero point calibration.

FIG. 18B is a graph that illustrates one example of using prior information for slope and baseline. The x-axis represents reference glucose data (blood glucose) from a reference glucose source in mg/dL; the y-axis represents sensor data from a transcutaneous glucose sensor of the preferred embodiments in counts. An upper boundary line 215 is a regression line that represents an upper boundary of “acceptability” in this example; the lower boundary line 216 is a regression line that represents a lower boundary of “acceptability” in this example. The boundary lines 215, 216 were obtained from retrospective analysis of in vivo sensitivities and baselines of glucose sensors as described in the preferred embodiments.

A plurality of matched data pairs 217 represents data pairs in a calibration set obtained from a glucose sensor as described in the preferred embodiments. The matched data pairs are plotted according to their sensor data and time-corresponding reference glucose data. A regression line 218 represents the result of regressing the matched data pairs 217 using least squares regression. In this example, the regression line falls within the upper and lower boundaries 215, 216 indicating that the sensor calibration is acceptable.

However, if the slope and/or baseline had fallen outside the predetermined acceptable boundaries, which would be illustrated in this graph by a line that crosses the upper and/or lower boundaries 215, 216, then the system is configured to assume a baseline value and re-run the regression (or a modified version of the regression) with the assumed baseline, wherein the assumed baseline value is derived from in vivo or in vitro testing. Subsequently, the newly derived slope and baseline are again tested to determine whether they fall within the predetermined acceptable boundaries. Similarly, the processing continues in response to the results of the boundary test. In general, for a set of matched pairs (e.g., calibration set), regression lines with higher slope (sensitivity) have a lower baseline and regression lines with lower slope (sensitivity) have a higher baseline. Accordingly, the step of assuming a baseline and testing against boundaries can be repeated using a variety of different assumed baselines based on the baseline, sensitivity, in vitro testing, and/or in vivo testing. For example, if a boundary test fails due to high sensitivity, then a higher baseline is assumed and the regression re-run and boundary-tested. It is preferred that after about two iterations of assuming a baseline and/or sensitivity and running a modified regression, the system assumes an error has occurred (if the resulting regression lines fall outside the boundaries) and fail-safe. The term “fail-safe” includes modifying the system processing and/or display of data responsive to a detected error avoid reporting of inaccurate or clinically irrelevant analyte values.

In these various embodiments utilizing an additional electrode, prior information (e.g. in vitro or in vivo testing), signal processing, or other information for assisting in the calibration process can be used alone or in combination to reduce or eliminate the dependency of the calibration on reference analyte values obtained by the host.

At block 212, a sensor data transformation module, also referred to as the calibration module, conversion module, or processor module, uses the conversion function to transform sensor data into substantially real-time analyte value estimates, also referred to as calibrated data, or converted sensor data, as sensor data is continuously (or intermittently) received from the sensor. For example, the sensor data, which can be provided to the receiver in “counts,” is translated in to estimate analyte value(s) in mg/dL. In other words, the offset value at any given point in time can be subtracted from the raw value (e.g. in counts) and divided by the slope to obtain the estimate analyte value:

${{mg}\text{/}{dL}} = \frac{\left( {{rawvalue} - {offset}} \right)}{slope}$

In one embodiment, the conversion function can be used to estimate analyte values for a future time period by forward projection. In alternative preferred embodiments, such as are described with reference to FIGS. 24 to 40, the processor can provide intelligent estimation, including dynamic determination of an algorithm, physiological boundaries, evaluation of the estimative algorithm, analysis of variations associated with the estimation, and comparison of measured analyte values with time corresponding estimated analyte values.

In some alternative embodiments, the sensor and/or reference analyte values are stored in a database for retrospective analysis.

In certain embodiments, the sensor data transformation module only converts sensor data points into calibrated data points when a certain analyte value condition is satisfied. Such a condition can include any of the conditions discussed above with reference to embodiments pre-screening or conditionally accepting reference analyte value data at block 204, with reference to selecting data pairs at block 208, or with reference to creating a conversion function at block 210.

At block 214, an output module provides output to the user via the user interface. The output is representative of the estimated analyte value, which is determined by converting the sensor data into a meaningful analyte value. User output can be in the form of a numeric estimated analyte value, an indication of directional trend of analyte concentration, and/or a graphical representation of the estimated analyte data over a period of time, for example. Other representations of the estimated analyte values are also possible, for example audio and tactile.

In one exemplary embodiment, such as is shown in FIG. 16A, the estimated analyte value is represented by a numeric value. In other exemplary embodiments, such as are shown in FIGS. 16B to 16D, the user interface graphically represents the estimated analyte data trend over predetermined a time period (e.g., one, three, and nine hours, respectively). In alternative embodiments, other time periods can be represented.

In some embodiments, the user interface begins displaying data to the user after the sensor's stability has been affirmed. In some alternative embodiments, however, the user interface displays data that is somewhat unstable (e.g., does not have sufficient stability and/or accuracy); in these embodiments, the receiver may also include an indication of instability of the sensor data (e.g., flashing, faded, or another indication of sensor instability displayed on the user interface). In some embodiments, the user interface informs the user of the status of the stability of the sensor data.

Accordingly, after initial calibration of the sensor, and optionally determination of stability of the sensor data, real-time continuous analyte information can be displayed on the user interface so that the user can regularly and proactively care for his/her diabetic condition within the bounds set by his/her physician.

In alternative embodiments, the conversion function is used to predict analyte values at future points in time. These predicted values can be used to alert the user of upcoming hypoglycemic or hyperglycemic events. Additionally, predicted values can be used to compensate for the time lag (e.g., 15 minute time lag such as described elsewhere herein), so that an estimated analyte value displayed to the user represents the instant time, rather than a time delayed estimated value.

In some embodiments, the substantially real time estimated analyte value, a predicted future estimate analyte value, a rate of change, and/or a directional trend of the analyte concentration is used to control the administration of a constituent to the user, including an appropriate amount and time, in order to control an aspect of the user's biological system. One such example is a closed loop glucose sensor and insulin pump, wherein the analyte data (e.g., estimated glucose value, rate of change, and/or directional trend) from the glucose sensor is used to determine the amount of insulin, and time of administration, that may be given to a diabetic user to evade hyper- and hypoglycemic conditions.

In some embodiments, annotations are provided on the graph; for example, bitmap images are displayed thereon, which represent events experienced by the host. For example, information about meals, insulin, exercise, sensor insertion, sleep, and the like, can be obtained by the receiver (by user input or receipt of a transmission from another device) and displayed on the graphical representation of the host's glucose over time. It is believed that illustrating a host's life events matched with a host's glucose concentration over time can be helpful in educating the host to his or her metabolic response to the various events.

In yet another alternative embodiment, the sensor utilizes one or more additional electrodes to measure an additional analyte. Such measurements can provide a baseline or sensitivity measurement for use in calibrating the sensor. Furthermore, baseline and/or sensitivity measurements can be used to trigger events such as digital filtering of data or suspending display of data, all of which are described in more detail in U.S. Patent Publication No. US-2005-0143635-A1.

Accordingly, after initial calibration of the sensor, continuous analyte values can be displayed on the user interface so that the user can regularly and proactively care for his/her diabetic condition within the bounds set by his/her physician. Both the reference analyte data and the sensor analyte data from the continuous analyte sensor can be displayed to the user. In an embodiment wherein the continuous analyte sensor functions as an adjunctive device to a reference analyte monitor, the user interface can display numeric reference analyte data, while showing the sensor analyte data only in a graphical representation so that the user can see the historical and present sensor trend information as well as the most recent reference analyte data value. In an embodiment wherein the continuous analyte sensor functions as a non-adjunctive device to the reference analyte monitor, the user interface can display the reference analyte data and/or the sensor analyte data. The user can toggle through menus and screens using the buttons in order to view alternate data and/or screen formats, for example.

In alternative embodiments, the output module displays the estimated analyte values in a manner such as are described in more detail with reference to FIGS. 41 to 48, for example. In some embodiments, the measured analyte value, an estimated future analyte value, a rate of change, and/or a directional trend of the analyte concentration is used to control the administration of a constituent to the user, including an appropriate amount and time, in order to control an aspect of the user's biological system. One such example is a closed loop glucose sensor and insulin pump, wherein the glucose data (for example, estimated glucose value, rate of change, and/or directional trend) from the glucose sensor is used to determine the amount of insulin, and time of administration, that can be given to a person with diabetes to evade hyperglycemic and hypoglycemic conditions. Output to external devices is described in more detail with reference to FIGS. 53 to 36, for example.

FIG. 19A provides a flow chart 220 that illustrates a process which, for example, a stability module can use in the evaluation of reference and/or sensor data for stability, and/or for statistical, clinical, and/or physiological acceptability. Although some acceptability tests are disclosed herein, any known statistical, clinical, physiological standards and methodologies can be applied to evaluate the acceptability of reference and sensor analyte data.

In some embodiments, a stability determination module is provided, also referred to as the start-up module or processor module, which determines the stability of the analyte sensor over a period of time. Some analyte sensors may have an initial instability time period during which the analyte sensor is unstable for environmental, physiological, or other reasons. Initial sensor instability can occur, for example, when the analyte sensor is implanted subcutaneously; stabilization of the analyte sensor can be dependent upon the maturity of the tissue ingrowth around and within the sensor. Initial sensor instability can also occur when the analyte sensor is implemented transdermally; stabilization of the analyte sensor can be dependent upon electrode stabilization and/or the presence of sweat, for example.

Accordingly, in some embodiments, achieving sensor stability can include waiting a predetermined time period (e.g. an implantable subcutaneous sensor can require a time period for tissue growth, and a transcutaneous sensor can require time to equilibrate the sensor with the user's skin). In some embodiments, this predetermined waiting period for a transcutaneous sensor is from about one minute to about six days, preferably from about 1 day to about five days, and more preferably from about two days to about four days. In other embodiments, the waiting period for a transcutaneous sensor is preferably from about 30 minutes to about 24 hours, more preferably from about one hour to about 12 hours, and most preferably from about 2 hours to about 10 hours. In some embodiments, this predetermined waiting period for a subcutaneous sensor is from about 1 day to about six weeks, preferably from about 1 week to about five weeks, and more preferably about from two weeks to about four weeks. In some embodiments, the sensitivity (e.g., sensor signal strength with respect to analyte concentration) can be used to determine the stability of the sensor; for example, amplitude and/or variability of sensor sensitivity can be evaluated to determine the stability of the sensor. In alternative embodiments, detection of pH levels, oxygen, hypochlorite, interfering species (e.g. ascorbate, urea, and/or acetaminophen), correlation between sensor and reference values (e.g. R-value), baseline drift and/or offset, and the like can be used to determine the stability of the sensor. In one exemplary embodiment, wherein the sensor is a glucose sensor, a signal can be provided that is associated with interfering species (e.g. ascorbate, urea, acetaminophen and/or the like), which can be used to evaluate sensor stability. In another exemplary embodiment, wherein the sensor is a glucose sensor, the counter electrode can be monitored for oxygen deprivation, which can be used to evaluate sensor stability or functionality.

In some embodiments, the system (e.g. microprocessor) determines whether the analyte sensor is sufficiently stable according to certain criteria, such as are described above with reference to FIG. 18A. In one embodiment wherein the sensor is an implantable glucose sensor, the system waits a predetermined time period for sufficient tissue ingrowth and evaluates the sensor sensitivity (e.g. from about one minute to six weeks). In another embodiment, the receiver determines sufficient stability based on oxygen concentration near the sensor head. In yet another embodiment, the sensor determines sufficient stability based on a reassessment of baseline drift and/or offset. In yet another alternative embodiment, the system evaluates stability by monitoring the frequency content of the sensor data stream over a predetermined amount of time (e.g. 24 hours); in this alternative embodiment, a template (or templates) are provided that reflect acceptable levels of glucose physiology and are compared with the actual sensor data, wherein a predetermined degree of agreement between the template and the actual sensor data is indicative of sensor stability. A few examples of determinations of sufficient stability are described herein; however, a variety of known tests and parameters can be used to determine sensor stability without departing from the spirit and scope of the preferred embodiments. If the stability is determined to be insufficient, additional sensor data can be repeatedly taken at predetermined intervals until a sufficient degree of stability is achieved.

In some embodiments, a clinical acceptability evaluation module, also referred to as clinical module, evaluates the clinical acceptability of newly received reference data and/or time corresponding sensor data. In some embodiments clinical acceptability criteria can include any of the conditions discussed above with reference to FIG. 18A as to pre-screening or conditionally accepting reference analyte value data. In some embodiments of evaluating clinical acceptability, the rate of change of the reference data as compared to previously obtained data is assessed for clinical acceptability. That is, the rate of change and acceleration (or deceleration) of the concentration of many analytes in vivo have certain physiological limits within the body. Accordingly, a limit can be set to determine if the new matched pair is within a physiologically feasible range, indicated by a rate of change from the previous data that is within known physiological and/or statistical limits. Similarly, in some embodiments an algorithm that predicts a future value of an analyte can be used to predict and then compare an actual value to a time corresponding predicted value to determine if the actual value falls within a clinically acceptable range based on the predictive algorithm, for example.

In one exemplary embodiment, the clinical acceptability evaluation module matches the reference data with a substantially time corresponding converted sensor value, and plots the matched data on a Clarke Error Grid. Such a Clarke Error Grid is described in more detail with reference to FIG. 19B, which is a graph of two data pairs on a Clarke Error Grid that illustrates the evaluation of clinical acceptability in one exemplary embodiment. The Clarke Error Grid can be used by the clinical acceptability evaluation module to evaluate the clinical acceptability of the disparity between a reference glucose value and a sensor glucose (e.g. estimated glucose) value, if any, in an embodiment wherein the sensor is a glucose sensor. The x-axis represents glucose reference glucose data and the y-axis represents estimated glucose sensor data. Matched data pairs are plotted accordingly to their reference and sensor values, respectively. In this embodiment, matched pairs that fall within the A and B regions of the Clarke Error Grid are considered clinically acceptable, while matched pairs that fall within the C, D, and E regions of the Clarke Error Grid are not considered clinically acceptable. Particularly, FIG. 19B shows a first matched pair 1992 is shown which falls within the A region of the Clarke Error Grid, and therefore is considered clinically acceptable. A second matched pair 1994 is shown which falls within the C region of the Clarke Error Grid, and therefore is not considered clinically acceptable.

A variety of other known methods of evaluating clinical acceptability can be utilized. In one alternative embodiment, the Consensus Grid is used to evaluate the clinical acceptability of reference and sensor data. In another alternative embodiment, a mean absolute difference calculation can be used to evaluate the clinical acceptability of the reference data. In another alternative embodiment, the clinical acceptability can be evaluated using any relevant clinical acceptability test, such as a known grid (e.g. Clarke Error or Consensus), and can include additional parameters such as time of day and/or an increasing or decreasing trend of the analyte concentration. In another alternative embodiment, a rate of change calculation can be used to evaluate clinical acceptability. In yet another alternative embodiment, wherein the reference data is received in substantially real time, the conversion function can be used to predict an estimated glucose value at a time corresponding to the time stamp of the reference analyte value (e.g. when there is a time lag of the sensor data such as described elsewhere herein). Accordingly, a threshold can be set for the predicted estimated glucose value and the reference analyte value disparity, if any.

The conventional analyte meters (e.g. self-monitored blood analyte tests) are known to have a ±20% error in analyte values. Gross errors in analyte readings are known to occur due to patient error in self-administration of the blood analyte test. For example, if the user has traces of sugar on his/her finger while obtaining a blood sample for a glucose concentration test, then the measured glucose value is likely to be much higher than the actual glucose value in the blood. Additionally, it is known that self-monitored analyte tests (e.g. test strips) are occasionally subject to manufacturing defects.

Another cause for error includes infrequency and time delay that may occur if a user does not self-test regularly, or if a user self-tests regularly but does not enter the reference value at the appropriate time or with the appropriate time stamp. Therefore, it can be advantageous to validate the acceptability of reference analyte values prior to accepting them as valid entries. Accordingly, the receiver evaluates the clinical acceptability of received reference analyte data prior to their acceptance as a valid reference value.

In one embodiment, the reference analyte data (and/or sensor analyte data) is evaluated with respect to substantially time corresponding sensor data (and/or substantially time corresponding reference analyte data) to determine the clinical acceptability of the reference analyte and/or sensor analyte data. A determination of clinical acceptability considers a deviation between time corresponding glucose measurements (e.g., data from a glucose sensor and data from a reference glucose monitor) and the risk (e.g., to the decision making of a diabetic patient) associated with that deviation based on the glucose value indicated by the sensor and/or reference data. Evaluating the clinical acceptability of reference and sensor analyte data, and controlling the user interface dependent thereon, can minimize clinical risk.

In one embodiment, the receiver evaluates clinical acceptability each time reference data is obtained. In another embodiment, the receiver evaluates clinical acceptability after the initial calibration and stabilization of the sensor. In some embodiments, the receiver evaluates clinical acceptability as an initial pre-screen of reference analyte data, for example after determining if the reference glucose measurement is between about 40 and 400 mg/dL. In other embodiments, other methods of pre-screening data can be used, for example by determining if a reference analyte data value is physiologically feasible based on previous reference analyte data values (e.g., below a maximum rate of change).

In some embodiments, a calibration evaluation module evaluates the new matched pair(s) for selective inclusion into the calibration set. In some embodiments, the receiver simply adds the updated matched data pair into the calibration set, displaces the oldest and/or least concordant matched pair from the calibration set, and proceeds to recalculate the conversion function accordingly.

In some embodiments, the calibration evaluation includes evaluating only the new matched data pair. In some embodiments, the calibration evaluation includes evaluating all of the matched data pairs in the existing calibration set and including the new matched data pair; in such embodiments not only is the new matched data pair evaluated for inclusion (or exclusion), but additionally each of the data pairs in the calibration set are individually evaluated for inclusion (or exclusion). In some alternative embodiments, the calibration evaluation includes evaluating all possible combinations of matched data pairs from the existing calibration set and including the new matched data pair to determine which combination best meets the inclusion criteria. In some additional alternative embodiments, the calibration evaluation includes a combination of at least two of the above-described evaluation method.

Inclusion criteria include at least one criterion that defines a set of matched data pairs that form a substantially optimal calibration set. Such criteria can include any of the conditions discussed above with reference to FIG. 18A concerning methods of pre-screening or conditionally accepting reference analyte value data. One inclusion criterion involves the time stamp of the matched data pairs (that make up the calibration set) spanning at least a predetermined time period (e.g. three hours). Another inclusion criterion involves the time stamps of the matched data pairs not being more than a predetermined age (e.g. one week old). Another inclusion criterion involves the matched pairs of the calibration set having a substantially evenly distributed amount of high and low raw sensor data, estimated sensor analyte values, and/or reference analyte values. Another criterion involves all raw sensor data, estimated sensor analyte values, and/or reference analyte values being within a predetermined range (e.g. 40 to 400 mg/dL for glucose values). Another criterion involves a rate of change of the analyte concentration (e.g. from sensor data) during the time stamp of the matched pair(s). For example, sensor and reference data obtained during the time when the analyte concentration is undergoing a slow rate of change is typically less susceptible to inaccuracies caused by time lag and other physiological and non-physiological effects. Another criterion involves the congruence of respective sensor and reference data in each matched data pair; the matched pairs with the most congruence are chosen. Another criterion involves physiological changes (e.g. low oxygen due to a user's posture that may effect the function of a subcutaneously implantable analyte sensor) to ascertain a likelihood of error in the sensor value. Evaluation of calibration set criteria can involve evaluating one, some, or all of the above described inclusion criteria. It is contemplated that additional embodiments can comprise additional inclusion criteria not explicitly described herein.

In some embodiments, a quality evaluation module evaluates the quality of the calibration. In one embodiment, the quality of the calibration is based on the association of the calibration set data using statistical analysis. Statistical analysis can include any known cost function, such as linear regression, non-linear mapping/regression, rank (e.g., non-parametric) correlation, least mean square fit, mean absolute deviation (MAD), mean absolute relative difference, and the like. The result of the statistical analysis provides a measure of the association of data used in calibrating the system. A threshold of data association can be set to determine if sufficient quality is exhibited in a calibration set.

In another embodiment, the quality of the calibration is determined by evaluating the calibration set for clinical acceptability, such as, for example using a Clarke Error Grid, Consensus Grid, or clinical acceptability test. As an example, the matched data pairs that form the calibration set can be plotted on a Clarke Error Grid, such that when all matched data pairs fall within the A and B regions of the Clarke Error Grid, then the calibration is determined to be clinically acceptable.

In yet another alternative embodiment, the quality of the calibration is determined based initially on the association of the calibration set data using statistical analysis, and then by evaluating the calibration set for clinical acceptability. If the calibration set fails the statistical and/or the clinical test, the calibration processing recalculates the conversion function with a new (e.g. optimized) set of matched data pairs. In this embodiment, the processing loop iterates until the quality evaluation module: 1) determines clinical acceptability; 2) determines sufficient statistical data association; 3) determines both clinical acceptability and sufficient statistical data association; or 4) surpasses a threshold of iterations.

Calibration of analyte sensors can be variable over time; that is, the conversion function suitable for one point in time may not be suitable for another point in time (e.g. hours, days, weeks, or months later). For example, in an embodiment wherein the analyte sensor is subcutaneously implantable, the maturation of tissue ingrowth over time can cause variability in the calibration of the analyte sensor. As another example, physiological changes in the user (e.g. metabolism, interfering blood constituents, and lifestyle changes) can cause variability in the calibration of the sensor. Accordingly, a continuously updating calibration algorithm that includes reforming the calibration set, and thus recalculating the conversion function, over time according to a set of inclusion criteria is advantageous.

One cause for discrepancies in reference and sensor data is a sensitivity drift that can occur over time, when a sensor is inserted into a host and cellular invasion of the sensor begins to block transport of the analyte to the sensor, for example. Therefore, it can be advantageous to validate the acceptability of converted sensor data against reference analyte data, to determine if a drift of sensitivity has occurred and whether the calibration should be updated.

In one embodiment, the reference analyte data is evaluated with respect to substantially time corresponding converted sensor data to determine the acceptability of the matched pair. For example, clinical acceptability considers a deviation between time corresponding analyte measurements (for example, data from a glucose sensor and data from a reference glucose monitor) and the risk (for example, to the decision making of a person with diabetes) associated with that deviation based on the glucose value indicated by the sensor and/or reference data. Evaluating the clinical acceptability of reference and sensor analyte data, and controlling the user interface dependent thereon, can minimize clinical risk. Preferably, the receiver evaluates clinical acceptability each time reference data is obtained.

After initial calibration, such as is described in more detail with reference to FIG. 18, the sensor data receiving module 222 receives substantially continuous sensor data (e.g. a data stream) via a receiver and converts that data into estimated analyte values. As used herein, the term “substantially continuous” is a broad term and is used in its ordinary sense, without limitation, to refer to a data stream of individual measurements taken at time intervals (e.g. time-spaced) ranging from fractions of a second up to, e.g. 1, 2, or 5 minutes or more. As sensor data is continuously converted, it can be occasionally recalibrated in response to changes in sensor sensitivity (drift), for example. Initial calibration and re-calibration of the sensor require a reference analyte value. Accordingly, the receiver can receive reference analyte data at any time for appropriate processing.

At block 222, the reference data receiving module, also referred to as the reference input module, receives reference analyte data from a reference analyte monitor. In one embodiment, the reference data comprises one analyte value obtained from a reference monitor. In some alternative embodiments however, the reference data includes a set of analyte values entered by a user into the interface and averaged by known methods, such as are described elsewhere herein. In some alternative embodiments, the reference data comprises a plurality of analyte values obtained from another continuous analyte sensor.

The reference data can be pre-screened according to environmental and physiological issues, such as time of day, oxygen concentration, postural effects, and patient-entered environmental data. In one exemplary embodiment, wherein the sensor comprises an implantable glucose sensor, an oxygen sensor within the glucose sensor is used to determine if sufficient oxygen is being provided to successfully complete the necessary enzyme and electrochemical reactions for accurate glucose sensing. In another exemplary embodiment, the patient is prompted to enter data into the user interface, such as meal times and/or amount of exercise, which can be used to determine likelihood of acceptable reference data. In yet another exemplary embodiment, the reference data is matched with time-corresponding sensor data, which is then evaluated on a modified clinical error grid to determine its clinical acceptability.

Some evaluation data, such as described in the paragraph above, can be used to evaluate an optimum time for reference analyte measurement. Correspondingly, the user interface can then prompt the user to provide a reference data point for calibration within a given time period. Consequently, because the receiver proactively prompts the user during optimum calibration times, the likelihood of error due to environmental and physiological limitations can decrease and consistency and acceptability of the calibration can increase.

At block 224, the evaluation module, also referred to as acceptability module, evaluates newly received reference data. In one embodiment, the evaluation module evaluates the clinical acceptability of newly received reference data and time corresponding converted sensor data (new matched data pair). In one embodiment, a clinical acceptability evaluation module 224 matches the reference data with a substantially time corresponding converted sensor value, and determines the Clarke Error Grid coordinates. In this embodiment, matched pairs that fall within the A and B regions of the Clarke Error Grid are considered clinically acceptable, while matched pairs that fall within the C, D, and E regions of the Clarke Error Grid are not considered clinically acceptable.

A variety of other known methods of evaluating clinical acceptability can be utilized. In one alternative embodiment, the Consensus Grid is used to evaluate the clinical acceptability of reference and sensor data. In another alternative embodiment, a mean absolute difference calculation can be used to evaluate the clinical acceptability of the reference data. In another alternative embodiment, the clinical acceptability can be evaluated using any relevant clinical acceptability test, such as a known grid (e.g. Clarke Error or Consensus), and additional parameters, such as time of day and/or the increase or decreasing trend of the analyte concentration. In another alternative embodiment, a rate of change calculation can be used to evaluate clinical acceptability. In yet another alternative embodiment, wherein the received reference data is in substantially real time, the conversion function could be used to predict an estimated glucose value at a time corresponding to the time stamp of the reference analyte value (this can be required due to a time lag of the sensor data such as described elsewhere herein). Accordingly, a threshold can be set for the predicted estimated glucose value and the reference analyte value disparity, if any. In some alternative embodiments, the reference data is evaluated for physiological and/or statistical acceptability as described in more detail elsewhere herein.

At decision block 226, results of the evaluation are assessed. If acceptability is determined, then processing continues to block 228 to re-calculate the conversion function using the new matched data pair in the calibration set.

At block 228, the conversion function module re-creates the conversion function using the new matched data pair associated with the newly received reference data. In one embodiment, the conversion function module adds the newly received reference data (e.g. including the matched sensor data) into the calibration set, and recalculates the conversion function accordingly. In alternative embodiments, the conversion function module displaces the oldest, and/or least concordant matched data pair from the calibration set, and recalculates the conversion function accordingly.

At block 230, the sensor data transformation module uses the new conversion function (from block 228) to continually (or intermittently) convert sensor data into estimated analyte values, also referred to as calibrated data, or converted sensor data, such as is described in more detail above.

At block 232, an output module provides output to the user via the user interface. The output is representative of the estimated analyte value, which is determined by converting the sensor data into a meaningful analyte value. User output can be in the form of a numeric estimated analyte value, an indication of directional trend of analyte concentration, and/or a graphical representation of the estimated analyte data over a period of time, for example. Other representations of the estimated analyte values are also possible, for example audio and tactile.

If, however, acceptability is determined at decision block 226 as negative (unacceptable), then the processing progresses to block 234 to adjust the calibration set. In one embodiment of a calibration set adjustment, the conversion function module removes one or more oldest matched data pair(s) and recalculates the conversion function accordingly. In an alternative embodiment, the conversion function module removes the least concordant matched data pair from the calibration set, and recalculates the conversion function accordingly.

At block 236, the conversion function module re-creates the conversion function using the adjusted calibration set. While not wishing to be bound by theory, it is believed that removing the least concordant and/or oldest matched data pair(s) from the calibration set can reduce or eliminate the effects of sensor sensitivity drift over time, adjusting the conversion function to better represent the current sensitivity of the sensor.

At block 224, the evaluation module re-evaluates the acceptability of newly received reference data with time corresponding converted sensor data that has been converted using the new conversion function (block 236). The flow continues to decision block 238 to assess the results of the evaluation, such as described with reference to decision block 226, above. If acceptability is determined, then processing continues to block 230 to convert sensor data using the new conversion function and continuously display calibrated sensor data on the user interface.

If, however, acceptability is determined at decision block 226 as negative, then the processing loops back to block 234 to adjust the calibration set once again. This process can continue until the calibration set is no longer sufficient for calibration, for example, when the calibration set includes only one or no matched data pairs with which to create a conversion function. In this situation, the system can return to the initial calibration or start-up mode, which is described in more detail with reference to FIGS. 18 and 21, for example. Alternatively, the process can continue until inappropriate matched data pairs have been sufficiently purged and acceptability is positively determined.

In alternative embodiments, the acceptability is determined by a quality evaluation, for example, calibration quality can be evaluated by determining the statistical association of data that forms the calibration set, which determines the confidence associated with the conversion function used in calibration and conversion of raw sensor data into estimated analyte values. See, e.g. U.S. Patent Publication No. US-2005-0027463-A1.

Alternatively, each matched data pair can be evaluated based on clinical or statistical acceptability such as described above; however, when a matched data pair does not pass the evaluation criteria, the system can be configured to ask for another matched data pair from the user. In this way, a secondary check can be used to determine whether the error is more likely due to the reference glucose value or to the sensor value. If the second reference glucose value substantially correlates to the first reference glucose value, it can be presumed that the reference glucose value is more accurate and the sensor values are errant. Some reasons for errancy of the sensor values include a shift in the baseline of the signal or noise on the signal due to low oxygen, for example. In such cases, the system can be configured to re-initiate calibration using the secondary reference glucose value. If, however, the reference glucose values do not substantially correlate, it can be presumed that the sensor glucose values are more accurate and the reference glucose values eliminated from the algorithm.

FIG. 20 provides is a flow chart 250 that illustrates the evaluation of calibrated sensor data for aberrant values in one embodiment. Although sensor data are typically accurate and reliable, it can be advantageous to perform a self-diagnostic check of the calibrated sensor data prior to displaying the analyte data on the user interface.

One reason for anomalies in calibrated sensor data includes transient events, such as local ischemia at the implant site, which can temporarily cause erroneous readings caused by insufficient oxygen to react with the analyte. Accordingly, the flow chart 190 illustrates one self-diagnostic check that can be used to catch erroneous data before displaying it to the user.

At block 252, a sensor data receiving module, also referred to as the sensor data module, receives new sensor data from the sensor.

At block 24, the sensor data transformation module continuously (or intermittently) converts new sensor data into estimated analyte values, also referred to as calibrated data.

At block 256, a self-diagnostic module compares the new calibrated sensor data with previous calibrated sensor data, for example, the most recent calibrated sensor data value. In comparing the new and previous sensor data, a variety of parameters can be evaluated. In one embodiment, the rate of change and/or acceleration (or deceleration) of change of various analytes, which have known physiological limits within the body, and sensor data can be evaluated accordingly. For example, a limit can be set to determine if the new sensor data is within a physiologically feasible range, indicated by a rate of change from the previous data that is within known physiological (and/or statistical) limits. Similarly, any algorithm that predicts a future value of an analyte can be used to predict and then compare an actual value to a time corresponding predicted value to determine if the actual value falls within a statistically and/or clinically acceptable range based on the predictive algorithm, for example. In certain embodiments, identifying a disparity between predicted and measured analyte data can be used to identify a shift in signal baseline responsive to an evaluated difference between the predicted data and time-corresponding measured data. In some alternative embodiments, a shift in signal baseline and/or sensitivity can be determined by monitoring a change in the conversion function; namely, when a conversion function is re-calculated using the equation y=mx+b, a change in the values of m (sensitivity) or b (baseline) above a pre-selected “normal” threshold, can be used to trigger a fail-safe or further diagnostic evaluation.

Although the above-described self-diagnostics are generally employed with calibrated sensor data, some alternative embodiments are contemplated that check for aberrancy of consecutive sensor values prior to sensor calibration, for example, on the raw data stream and/or after filtering of the raw data stream. In certain embodiments, an intermittent or continuous signal-to-noise measurement can be evaluated to determine aberrancy of sensor data responsive to a signal-to-noise ratio above a set threshold. In certain embodiments, signal residuals (e.g., by comparing raw and filtered data) can be intermittently or continuously analyzed for noise above a set threshold. In certain embodiments, pattern recognition can be used to identify noise associated with physiological conditions, such as low oxygen (see, e.g. U.S. Patent No. US-2005-0043598-A1), or other known signal aberrancies. Accordingly, in these embodiments, the system can be configured, in response to aberrancies in the data stream, to trigger signal estimation, adaptively filter the data stream according to the aberrancy, or the like, as described in more detail in the above cited U.S. Patent No. US-2005-0043598-A1.

In another embodiment, reference analyte values are processed to determine a level of confidence, wherein reference analyte values are compared to their time-corresponding calibrated sensor values and evaluated for clinical or statistical accuracy. In yet another alternative embodiment, new and previous reference analyte data are compared in place of or in addition to sensor data. In general, there exist known patterns and limitations of analyte values that can be used to diagnose certain anomalies in raw or calibrated sensor and/or reference analyte data.

Block 193 describes additional systems and methods that can by utilized by the self-diagnostics module of the preferred embodiments.

At decision block 258, the system determines whether the comparison returned aberrant values. In one embodiment, the slope (rate of change) between the new and previous sensor data is evaluated A change in concentration value of greater than +/−10%, +/−15%, +/−20%, +/−25%, or +/−30%; and/or a rate of change of glucose concentration of +/−6 mg/dL/min or more, preferably +/−5 or more, more preferably +/−4 mg/dL/min or more, even more preferably +/−3 mg/dL/min or more, and most preferably +/−2 mg/dL/min or more are generally considered aberrant. In certain embodiments, other known physiological parameters can be used to determine aberrant values. However, a variety of comparisons and limitations can be set.

At block 260, if the values are not found to be aberrant, the sensor data transformation module continuously (or intermittently) converts received new sensor data into estimated analyte values, also referred to as calibrated data.

At block 262, if the values are found to be aberrant, the system goes into a suspended mode, also referred to as fail-safe mode in some embodiments, which is described in more detail below with reference to FIG. 21. In general, suspended mode suspends display of calibrated sensor data and/or insertion of matched data pairs into the calibration set. Preferably, the system remains in suspended mode until received sensor data is not found to be aberrant. In certain embodiments, a time limit or threshold for suspension is set, after which system and/or user interaction can be required, for example, requesting additional reference analyte data, replacement of the electronics unit, and/or reset.

In some alternative embodiments, in response to a positive determination of aberrant value(s), the system can be configured to estimate one or more glucose values for the time period during which aberrant values exist. Signal estimation generally refers to filtering, data smoothing, augmenting, projecting, and/or other methods for estimating glucose values based on historical data, for example. In one implementation of signal estimation, physiologically feasible values are calculated based on the most recent glucose data, and the aberrant values are replaced with the closest physiologically feasible glucose values. See also U.S. Patent Publication No. US-2005-0027463-A1, U.S. Patent No. US-2005-0043598-A1, and U.S. Patent Publication No. US-2005-0203360-A1.

FIG. 21 provides a flow chart 280 that illustrates a self-diagnostic of sensor data in one embodiment. Although reference analyte values can useful for checking and calibrating sensor data, self-diagnostic capabilities of the sensor provide for a fail-safe for displaying sensor data with confidence and enable minimal user interaction (for example, requiring reference analyte values only as needed).

At block 282, a sensor data receiving module, also referred to as the sensor data module, receives new sensor data from the sensor.

At block 284, the sensor data transformation module continuously (or intermittently) converts received new sensor data into estimated analyte values, also referred to as calibrated data.

At block 286, a self-diagnostics module, also referred to as a fail-safe module, performs one or more calculations to determine the accuracy, reliability, and/or clinical acceptability of the sensor data. Some examples of the self-diagnostics module are described above, with reference block 256. The self-diagnostics module can be further configured to run periodically (e.g. intermittently or in response to a trigger), for example, on raw data, filtered data, calibrated data, predicted data, and the like.

In certain embodiments, the self-diagnostics module evaluates an amount of time since sensor insertion into the host, wherein a threshold is set for the sensor's usable life, after which time period the sensor is considered to be unreliable. In certain embodiments, the self-diagnostics module counts the number of times a failure or reset is required (for example, how many times the system is forced into suspended or start-up mode), wherein a count threshold is set for a predetermined time period, above which the system is considered to be unreliable. In certain embodiments, the self-diagnostics module compares newly received calibrated sensor data with previously calibrated sensor data for aberrant values, such as is described in more detail with reference to FIG. 5, above. In certain embodiments, the self-diagnostics module evaluates clinical acceptability, such as is described in more detail with reference to FIG. 20, above. In certain embodiments, diagnostics, such as are described in U.S. Pat. No. 7,081,195 and U.S. Patent Publication No. US-2005-0143635-A1 can be incorporated into the systems of preferred embodiments for system diagnosis, for example, for identifying interfering species on the sensor signal and for identifying drifts in baseline and sensitivity of the sensor signal.

In some embodiments, an interface control module, also referred to as the fail-safe module, controls the user interface based upon the clinical acceptability of the reference data received. If the reference data is not considered clinically acceptable, then a fail-safe module begins the initial stages of fail-safe mode. In some embodiments, the initial stages of fail-safe mode include altering the user interface so that estimated sensor data is not displayed to the user. In some embodiments, the initial stages of fail-safe mode include prompting the user to repeat the reference analyte test and provide another reference analyte value. The repeated analyte value is then evaluated for clinical acceptability.

If the results of the repeated analyte test are determined to be clinically unacceptable, then the fail-safe module can alter the user interface to reflect full fail-safe mode. In one embodiment, full fail-safe mode includes discontinuing sensor analyte display output on the user interface. In other embodiments, color-coded information, trend information, directional information (e.g., arrows or angled lines), gauges, and/or other fail-safe information can be displayed, for example.

The initial stages of fail-safe mode and full fail safe mode can include user interface control, for example. Additionally, it is contemplated herein that a variety of different modes between initial and full fail-safe mode can be provided, depending on the relative quality of the calibration. In other words, the confidence level of the calibration quality can control a plurality of different user interface screens providing error bars, ±values, and the like. Similar screens can be implemented in various clinical acceptability embodiments.

At block 288 of FIG. 21, a mode determination module, which can be a part of the sensor evaluation module 224, determines in which mode the sensor is set (or remains in). In some embodiments, the system is programmed with three modes: 1) start-up mode; 2) normal mode; and 3) suspended mode. Although three modes are described herein, the preferred embodiments are not limited to the number or types of modes with which the system can be programmed. In some embodiments, the system is defined as “in-cal” (in calibration) in normal mode; otherwise, the system is defined as “out-of-cal’ (out of calibration) in start-up and suspended mode. The terms as used herein are meant to describe the functionality and are not limiting in their definitions.

Preferably, a start-up mode is provided wherein the start-up mode is set when the system determines that it can no longer remain in suspended or normal mode (for example, due to problems detected by the self-diagnostics module, such as described in more detail above) and/or when the system is notified that a new sensor has been inserted. Upon initialization of start-up mode, the system ensures that any old matched data pairs and/or calibration information is purged. In start-up mode, the system initializes the calibration set, such as is described in more detail with reference to FIG. 14, above. Once the calibration set has been initialized, sensor data is ready for conversion and the system is set to normal mode.

Preferably, a normal mode is provided wherein the normal mode is set when the system is accurately and reliably converting sensor data, for example, wherein clinical acceptability is positively determined, aberrant values are negatively determined, and/or the self-diagnostics modules confirms reliability of data. In normal mode, the system continuously (or intermittently) converts (or calibrates) sensor data. Additionally, reference analyte values received by the system are matched with sensor data points and added to the calibration set.

In certain embodiments, the calibration set is limited to a predetermined number of matched data pairs, after which the systems purges old or less desirable matched data pairs when a new matched data pair is added to the calibration set. Less desirable matched data pairs can be determined by inclusion criteria, which include one or more criteria that define a set of matched data pairs that form a substantially optimal calibration set.

Unfortunately, some circumstances can exist wherein a system in normal mode is changed to start-up or suspended mode. In general, the system is programmed to change to suspended mode when a failure of clinical acceptability, aberrant value check, and/or other self-diagnostic evaluation is determined, such as described in more detail above, and wherein the system requires further processing to determine whether a system re-start is required (e.g. start-up mode). In general, the system changes to start-up mode when the system is unable to resolve itself in suspended mode and/or when the system detects that a new sensor has been inserted (e.g. via system trigger or user input).

Preferably, a suspended mode is provided wherein the suspended mode is set when a failure of clinical acceptability, aberrant value check, and/or other self-diagnostic evaluation determines unreliability of sensor data. In certain embodiments, the system enters suspended mode when a predetermined time period passes without receiving a reference analyte value. In suspended mode, the calibration set is not updated with new matched data pairs, and sensor data can optionally be converted, but not displayed on the user interface. The system can be changed to normal mode upon resolution of a problem (positive evaluation of sensor reliability from the self-diagnostics module, for example). The system can be changed to start-up mode when the system is unable to resolve itself in suspended mode and/or when the system detects a new sensor has been inserted (via system trigger or user input).

The systems of preferred embodiments, including a transcutaneous analyte sensor, mounting unit, electronics unit, applicator, and receiver for inserting the sensor, and measuring, processing, and displaying sensor data, provide improved convenience and accuracy because of their designed stability within the host's tissue with minimum invasive trauma, while providing a discreet and reliable data processing and display, thereby increasing overall host comfort, confidence, safety, and convenience. Namely, the geometric configuration, sizing, and material of the sensor of the preferred embodiments enable the manufacture and use of an atraumatic device for continuous measurement of analytes, in contrast to conventional continuous glucose sensors available to persons with diabetes, for example. Additionally, the sensor systems of preferred embodiments provide a comfortable and reliable system for inserting a sensor and measuring an analyte level for up to 7 days or more without surgery. The sensor systems of the preferred embodiments are designed for host comfort, with chemical and mechanical stability that provides measurement accuracy. Furthermore, the mounting unit is designed with a miniaturized and reusable electronics unit that maintains a low profile during use. The usable life of the sensor can be extended by incorporation of a bioactive agent into the sensor that provides local release of an anti-inflammatory, for example, in order to slow the subcutaneous foreign body response to the sensor.

After the usable life of the sensor (for example, due to a predetermined expiration, potential infection, or level of inflammation), the host can remove the transcutaneous sensor and mounting from the skin, and dispose of the sensor and mounting unit (preferably saving the electronics unit for reuse). Another transcutaneous sensor system can be inserted with the reusable electronics unit and thus provide continuous sensor output for long periods of time.

EXAMPLES

FIG. 22A is a graphical representation showing transcutaneous glucose sensor data and corresponding blood glucose values over time in a human. The x-axis represents time, the first y-axis represents current in picoAmps, and the second y-axis represents blood glucose in mg/dL. As depicted on the legend, the small diamond points represent the current measured from the working electrode of a transcutaneous glucose sensor of a preferred embodiment; while the larger points represent blood glucose values of blood withdrawn from a finger stick and analyzed using an in vitro self-monitoring blood glucose meter (SMBG).

A transcutaneous glucose sensor was built according to the preferred embodiments and implanted in a human host where it remained over a period of time. Namely, the sensor was built by providing a platinum wire, vapor-depositing the platinum with Parylene to form an insulating coating, helically winding a silver wire around the insulated platinum wire (to form a “twisted pair”), masking sections of the electroactive surface of the silver wire, vapor-depositing Parylene on the twisted pair, chloridizing the silver electrode to form silver chloride reference electrode, and removing a radial window on the insulated platinum wire to expose a circumferential electroactive working electrode surface area thereon, this assembly also referred to as a “parylene-coated twisted pair assembly.”

An interference domain was formed on the parylene-coated twisted pair assembly by dip coating in an interference domain solution (7 weight percent of a 50,000 molecular weight cellulose acetate (Sigma-Aldrich, St. Louis, Mo.) in a 2:1 acetone/ethanol solvent solution), followed by drying at room temperature for 3 minutes. This interference domain solution dip coating step was repeated two more times to form an interference domain comprised of 3 layers of cellulose acetate on the assembly. The dip length (insertion depth) was adjusted to ensure that the cellulose acetate covered from the tip of the working electrode, over the exposed electroactive working electrode window, to cover a distal portion of the exposed electroactive reference electrode.

An enzyme domain was formed over the interference domain by subsequently dip coating the assembly in an enzyme domain solution and drying in a vacuum oven for 20 minutes at 50° C. This dip coating process was repeated once more to form an enzyme domain having two layers. A resistance domain was formed over the interference domain by subsequently spray coating the assembly with a resistance domain solution and drying the assembly in a vacuum oven for 60 minutes at 50° C. Additionally, the sensors were exposed to electron beam radiation at a dose of 25 kGy, while others (control sensors) were not exposed to electron beam radiation.

The graph of FIG. 22A illustrates approximately 3 days of data obtained by the electronics unit operably connected to the sensor implanted in the human host. Finger-prick blood samples were taken periodically and glucose concentration measured by a blood glucose meter (SMBG). The graphs show the subcutaneous sensor data obtained by the transcutaneous glucose sensor tracking glucose concentration as it rose and fell over time. The time-corresponding blood glucose values show the correlation of the sensor data to the blood glucose data, indicating appropriate tracking of glucose concentration over time.

The raw data signal obtained from the sensor electronics has a current measurement in the picoAmp range. Namely, for every unit (mg/dL) of glucose, approximately 3.5 pA or less to 7.5 pA or more current is measured. Generally, the approximately 3.5 to 7.5 pA/mg/dL sensitivity exhibited by the device can be attributed to a variety of design factors, including resistance of the membrane system to glucose, amount of enzyme in the membrane system, surface area of the working electrode, and electronic circuitry design. Accordingly, a current in the picoAmp range enables operation of an analyte sensor that: 1) requires (or utilizes) less enzyme (e.g. because the membrane system is highly resistive and allows less glucose through for reaction in the enzyme domain); 2) requires less oxygen (e.g. because less reaction of glucose in the enzyme domain requires less oxygen as a co-reactant) and therefore performs better during transient ischemia of the subcutaneous tissue; and 3) accurately measures glucose even in hypoglycemic ranges (e.g. because the electronic circuitry is able to measure very small amounts of glucose (hydrogen peroxide at the working electrode)). Advantageously, the analyte sensors of the preferred embodiments exhibit improved performance over convention analyte sensors at least in part because a current in the picoAmp range enables operation in conditions of less enzyme, and less oxygen, better resolution, lower power usage, and therefore better performance in the hypoglycemic range wherein lower mg/dL values conventionally have yielded lower accuracy.

FIG. 22B is a graphical representation showing transcutaneous glucose sensor data and corresponding blood glucose values over time in a human. The x-axis represents time; the y-axis represents glucose concentration in mg/dL. As depicted on the legend, the small diamond points represent the calibrated glucose data measured from a transcutaneous glucose sensor of a preferred embodiment; while the larger points represent blood glucose values of blood withdrawn from a finger stick and analyzed using an in vitro self-monitoring blood glucose meter (SMBG). The calibrated glucose data corresponds to the data of FIG. 22A shown in current, except it has been calibrated using algorithms of the preferred embodiments. Accordingly, accurate subcutaneous measurement of glucose concentration has been measured and processed using the systems and methods of the preferred embodiments.

FIG. 23 is a graphical representation showing transcutaneous glucose sensor data and corresponding blood glucose values obtained over approximately seven days in a human. The x-axis represents time; the y-axis represents glucose concentration in mg/dL. As depicted on the legend, the small diamond points represent the calibrated glucose data measured from a transcutaneous glucose sensor of a preferred embodiment; while the larger points represent blood glucose values of blood withdrawn from a finger stick and analyzed using an in vitro self-monitoring blood glucose meter (SMBG). The calibrated glucose data corresponds to a sensor that was implanted in a human for approximately seven days, showing an extended functional life, as compare to three days, for example.

Differentiation of Sensor Systems

Some embodiments provide sensor systems suitable for implantation for 1, 3, 5, 7, or 10 days or more. Alternatively, sensors designed for shorter or longer durations can have one or more specific design features (e.g. membrane systems, bioactive agent(s), architecture, electronic design, power source, software, or the like) customized for the intended sensor life. Similarly, some embodiments provide sensor systems suitable for a variety of uses such as pediatrics, adults, geriatrics, persons with type-1 diabetes, persons with type-2 diabetes, intensive care (ICU), hospital use, home use, rugged wear, everyday wear, exercise, and the like, wherein the sensor systems include particular design features (e.g. membrane systems, bioactive agent(s), architecture, electronic design, power source, software, or the like) customized for an intended use. Accordingly, it can be advantageous to differentiate sensor systems that are substantially similar, for example, sensors wherein the electronics unit of a sensor system can releasably mate with different mounting units, or sensors wherein different electronics units designed for different functionality can mate with a specific mounting unit.

In some embodiments, the mechanical, electrical, and/or software design enables the differentiation (e.g. non-interchangeability) of these different sensor systems. In other words, the sensor systems can be “keyed” to ensure a proper match between an electronics unit and a mounting unit (housing including sensor) as described herein. The terms “key” and “keyed” as used herein are broad terms and are used in their ordinary sense, including, without limitation, to refer to systems and methods that control the operable connection or operable communication between the sensor, its associated electronics, the receiver, and/or its associated electronics. The terms are broad enough to include mechanical, electrical, and software “keys.” For example, a mechanically designed key can include a mechanical design that allows an operable connection between two parts, for example, a mating between the electronics unit and the mounting unit wherein the contacts are keyed to mutually engage contacts of complementary parts. As another example, an electronically designed key can include a radio frequency identification chip (RFID chip) on the mounting unit, wherein the electronics unit is programmed to identify a predetermined identification number (key) from the RFID chip prior to operable connection or communication between the sensor and/or sensor electronics. As yet another example, a software key can include a code or serial number that identifies a sensor and/or electronics unit.

Accordingly, systems and methods are provided for measuring an analyte in a host, including: a sensor configured for transcutaneous insertion into a host's tissue; a housing adapted for placement external to the host's tissue and for supporting the sensor; and an electronics unit releasably attachable to said housing, wherein at least one of the housing and the electronics unit are keyed to provide a match between the sensor and the electronics unit.

In some embodiments, the housing (including a sensor) and its matching electronics unit(s) are keyed by a configuration of the one or more contacts thereon. FIGS. 4A to 4C illustrate three unique contact configurations, wherein the configurations are differentiated by a distance between the first and second contacts located within the housing. In this embodiment, a properly keyed electronics unit is configured with contacts that mate with the contacts on a mating housing (FIGS. 4A to 4C), for example a narrow contact configuration on a housing mates only with a narrow contact configuration on an electronics unit. Accordingly, in practice, only an electronics unit comprising a contact configuration that is designed for mutual engagement with a similarly “keyed” housing can be operably connected thereto.

In some embodiments, the electronics unit is programmed with an ID, hereinafter referred to as a “transmitter ID,” that uniquely identifies a sensor system. In one exemplary embodiment, wherein a first sensor system is designed for 3-day use and a second sensor system is designed for 7-day use, the transmitter ID can be programmed to begin with a “3” or a “7” in order to differentiate the sensor systems. In practice, a 3-day sensor system is programmed for 3-day use (see enforcement of sensor expiration described in more detail below), and thus upon operable connection of a 3-day sensor system, the receiver can function for the appropriate duration according to the transmitter ID.

In some embodiments, each sensor system is associated with a unique or near-unique serial number, which is associated with one or a set of sensor systems. This serial number can include information such as intended duration, calibration information, and the like, so that upon sensor insertion, and operable connection of the sensor electronics, the serial number can be manually entered into the receiver (from the packaging, for example) or can be automatically transmitted from the sensor's electronics unit. In this way, the serial number can provide the necessary information to enable the sensor system to function for the intended duration.

Additionally or alternatively, the electronics unit and/or mounting unit can be labeled or coded, for example, alpha-numerically, pictorially, or colorfully, to differentiate unique sensor systems. In this way, a user is less likely to confuse different sensor systems.

Enforcement of Sensor Expiration (Duration of Sensor Life)

In general, transcutaneous sensor systems can be designed for a predetermined life span (e.g. a few hours to a few days or more). Some embodiments provide sensor systems suitable for 1-, 3-, 5-, 7- or 10-days or more. One potential problem that may occur in practice is the continued use of the sensor beyond its intended life; for example, a host may not remove the sensor after its intended life and/or the host can detach and reattach the electronics unit into the mounting unit (which may cause a refresh of the sensor system and/or use beyond its intended life in some circumstances). Accordingly, systems and methods are needed for ensuring the sensor system is used for its proper duration and that accidental or intentional efforts to improperly extend or reuse the sensor system are avoided.

The preferred embodiments provide systems and methods for measuring an analyte in a host, the system including: a sensor adapted for transcutaneous insertion through the skin of a host; a housing adapted for placement adjacent to the host's skin and for supporting the sensor upon insertion through the skin; and an electronics unit operably connected to the housing, wherein the sensor system is configured to prevent use of the sensor (e.g. to render the sensor inoperative) beyond a predetermined time period.

In some embodiments, the sensor system is configured to destroy the sensor when the electronics unit is removed and/or after a predetermined time period has expired. In one exemplary embodiment, a loop of material surrounds a portion of the sensor and is configured to retract the sensor (from the host) when the electronics unit is removed from the housing. In another embodiment, the sensor system is configured to cut, crimp, or otherwise destroy the sensor when the electronics unit is removed from the housing.

In some embodiments, the sensor system is programmed to determine whether to allow an initialization of a new sensor. For example, the receiver can be programmed to require the sensor be disconnected prior to initiation of the receiver for an additional sensor system. In one such embodiment, the receiver can be programmed to look for a zero from the electronics unit, indicating the sensor has been disconnected, prior to allowing a new sensor to be initiated. This can help to ensure that a user actually removes the electronics unit (and/or sensor) prior to initialization of a new sensor. In another such embodiment, sensor insertion information can be programmed into the sensor electronics, such that the sensor insertion information is transmitted to the receiver to allow initialization of a new sensor.

In some embodiments, the receiver software receives information from the electronics unit (e.g., intended duration, transmitter ID, expiration date, serial code, manufacture date, or the like) and is programmed to automatically shut down after a predetermined time period (intended duration) or sensor expiration, for example.

In some embodiments, the receiver is programmed to algorithmically identify a new sensor insertion by looking for change in signal characteristic (e.g., a spike indicating break-in period, no change in sensor count values during the first hour, or the like). If a user has not inserted a new sensor, then the continued use of an expired sensor can be detected and can be used to trigger a shut down of the sensor and/or receiver.

In some embodiments, each sensor system is associated with a unique or near-unique serial number, which is associated with one or a set of sensor systems as described in more detail above. In general, the serial number can include information such as calibration information, intended duration, manufacture date, expiration date, and the like. For example, the serial number can provide sensor life (intended duration) information, which can be used to shut down the sensor and/or receiver after the intended sensor life.

The above described systems and methods for differentiating sensor systems and enforcing sensor lifetimes can be used alone or in combination, and can be combined with any of the preferred embodiments.

Dynamic and Intelligent Analyte Value Estimation

Estimative algorithms can be applied continuously, or selectively turned on/off based on conditions. Conventionally, a data stream received from a continuous analyte sensor can provide an analyte value and output the same to the host, which can be used to warn a patient or doctor of existing clinical risk. Conventionally, a data stream received from an analyte sensor can provide historical trend analyte values, which can be used to educate a patient or doctor of individual historical trends of the patient's analyte concentration. However, the data stream cannot, without additional processing, provide future analyte values, which can be useful in preventing clinically risky analyte values, compensating for time lag, and ensuring proper matching of sensor and reference analyte, for example such as described below. Timelier reporting of analyte values and prevention of clinically risky analyte values, for example, prevention of hyper- and hypoglycemic conditions in a person with diabetes, can decrease health complications that can result from clinically risky situations.

FIG. 24 is a flow chart that illustrates the process 354 of estimating analyte values and outputting estimated analyte values in one embodiment. In this embodiment, estimation is used to calculate analyte data for time during which no data exists (for example, data gaps or future data) or to replace data when large inaccuracies are believed to exist within data (for example, signal noise due to transient ischemia). Estimation of analyte values can be performed instead of, or in combination with, calibration of measured analyte values.

The estimating analyte values process 354 can be applied continuously, or selectively turned on/off based on conditions. The determination of when to apply estimative algorithms is discussed in more detail below. In some embodiments, estimation can be applied only during approaching clinical risk to warn a patient or doctor in an effort to avoid the clinical risk, for example when the measured glucose concentration is outside of a clinically acceptable threshold (for example, 100 to 200 mg/dL) and/or the glucose concentration is increasing or decreasing at a certain rate of change (for example, 3 mg/dL/min), such as described in more detail with reference to FIG. 25, for example. In some embodiments estimation can be applied continuously, dynamically, or intermittently to compensate for a time lag associated with the analyte sensor, which time lag can be consistent, dynamic, and/or intermittent, such as described in more detail below with reference to FIGS. 26 to 27, for example. In some embodiments, estimation can be applied to aid in dynamically and intelligently matching reference data with corresponding sensor data to ensure accurate outlier detection and/or calibration of sensor data with reference data, such as described in more detail with reference to FIGS. 28 and 29, for example. In some embodiments, estimation can be applied continuously (or intermittently) in order to provide analyte data that encourages more timely proactive behavior in preempting clinical risk.

At a block 356, the estimate analyte values process 354 obtains sensor data, which can be raw, smoothed, and/or otherwise processed. In some embodiments, estimation can be applied to a raw data stream received from an analyte sensor, such as described in more detail elsewhere herein. In some embodiments, estimation can be applied to calibrated data, such as described in more detail elsewhere herein.

At a block 358, the estimate analyte values process 354 dynamically and intelligently estimates analyte values based on measured analyte values using estimative algorithms. In some embodiments, dynamic and intelligent estimation includes selecting an algorithm from a plurality of algorithms to determine an estimative algorithm (for example, first or second order regression) that best fits the present measured analyte values, such as described in more detail with reference to FIGS. 30 and 31, for example. In some embodiments, dynamic and intelligent estimation further includes constraining and/or expanding estimated analyte values using physiological parameters, such as described in more detail with reference to FIGS. 32 and 33, for example. In some embodiments, dynamic and intelligent estimation further includes evaluating the selected estimative algorithms, for example using a data association function, such as described in more detail with reference to FIGS. 30, 31, 34, and 35. In some embodiments, dynamic and intelligent estimation includes analyzing a possible variation associated with the estimated analyte values, for example using statistical, clinical, or physiological variations, such as described in more detail with reference to FIGS. 36 to 38. In some embodiments, dynamic and intelligent estimation includes comparing previously estimated analyte values with measured analyte values for a corresponding time period, determining the deviation, such as described with reference to FIGS. 39 and 40, for example. In some embodiments, the resulting deviation from the comparison can be used to determine a variation for future estimated analyte values. In some embodiments, the resulting deviation from the comparison can be used to determine a confidence level in the estimative algorithms. In some embodiments, the resulting deviation from the comparison can be used to show evidence of the benefits of displaying estimated analyte values on patient behavior, namely how well the patient responds to the estimated analyte values and alters his/her behavior in order to better control analyte levels.

At a block 360, the output module 178 provides output to the user interface 160 and/or the external device 180. In some embodiments, output of estimated analyte values is combined with output of measured analyte values, such as in more detail elsewhere herein, for example combined on an LCD screen, or by toggling between screens. In some embodiments, a target analyte value or range of analyte values is output to the user interface alone, or in combination with the estimated analyte values, in order to provide a goal towards which the user can aim, such as described with reference to FIGS. 43 to 45, for example. In some embodiments, an approaching clinical risk is output in the form of a visual, audible, or tactile prompt, such as described with reference to FIGS. 41 to 43, for example. In some embodiments, therapy recommendations are output to aid the user in determining corrective action that can be performed in an effort to avoid or minimize clinical risk such as described with reference to FIG. 45, for example. In some embodiments, a visual representation of possible variations of the estimated analyte values, which variation can be due to statistical, clinical, or physiological considerations, such as described with reference to FIGS. 45 to 47, for example. In some embodiments, the output prompts a user to obtain a reference analyte value (not shown). In some embodiments, output is sent to an external device such as described with reference to FIGS. 48 to 51, for example.

FIG. 25 is a graph that illustrates one embodiment, wherein estimation is triggered by an event such as a patient's blood glucose concentration rising above a predetermined threshold (for example, 180 mg/dL). The x-axis represents time in minutes; the y-axis represents glucose concentration in mg/dL. The graph shows an analyte trend graph, particularly, the graph shows measured glucose data 362 for about 90 minutes up to time (t)=0. In this embodiment, the measured glucose data 362 has been smoothed and calibrated, however smoothing and/or calibrating may not be required in some embodiments. At t=0, estimation of the preferred embodiments is invoked and 15-minute estimated glucose data 364 indicates that the glucose concentration will likely rise above 220 mg/dL. The estimated glucose data 364 can be useful in providing alarms (e.g., hyper- and hypoglycemic alerts) and/or displaying on the user interface of the receiver, for example. Alarms may not require estimative algorithms in some embodiments, for example when zero, first, and/or second order calculations can be made to dynamically assess the static value, rate of change, and/or rate of acceleration of the analyte data in some embodiment.

In some embodiments, estimative algorithms are selectively applied when the reference and/or sensor analyte data indicates that the analyte concentration is approaching clinical risk. The concentration of the analyte values, the rate of change of the analyte values, and/or the acceleration of the analyte values can provide information indicative of approaching clinical risk. In an example wherein the analyte sensor is a glucose sensor, thresholds (for example, 100 to 200 mg/dL) can be set that selectively turn on estimative algorithms that then dynamically and intelligently estimate upcoming glucose values, and optionally possible variations of those estimated glucose values, to appropriately forewarn of an upcoming patient clinical risk (for example, hypo- or hyperglycemia). Additionally, the rate of change and/or acceleration can be considered to more intelligently turn on and calculate necessary estimation and for alarms (e.g., hyper- and hypoglycemic alarms). For example, if a person with diabetes has a glucose concentration of 100 mg/dL, but is trending upwardly, has slow or no rate of change, or is decelerating downwardly, estimation and/or alarms may not be necessary.

FIG. 26 is a graph that illustrates a raw data stream and the corresponding reference analyte values. The x-axis represents time in minutes, the first y-axis represents sensor glucose data measured in counts, and the second y-axis represents reference glucose data in mg/dL. A raw data stream 366 was obtained for a host from a continuous glucose sensor over a 4-hour time period. In this example, the raw data stream 366 has not been smoothed, calibrated, or otherwise processed and is represented in counts. Reference glucose values 368 were obtained from the host using a reference glucose monitor during the same 4 -hour time period. The raw data stream 366 and reference glucose values 368 were plotted on the graph of FIG. 26 accordingly during the 4-hour time period. While not wishing to be bound by theory, the visible difference between the reference and sensor glucose data is believed to be caused at least in part by a time lag, such as described in more detail below.

A data stream received from an analyte sensor can include a time lag within the measured analyte concentration, for example, as compared to corresponding reference analyte values. In some embodiments, a time lag can be associated with a difference in measurement samples (for example, an interstitial fluid sample measured by an implantable analyte sensor as compared with a blood sample measured by an external reference analyte monitor). In some embodiments, a time lag can be associated with diffusion of the analyte through a membrane system, for example such as has been observed in some implantable electrochemically-based glucose sensors. Additionally in some embodiments, a time lag can be associated with processing of the data stream, for example, a finite impulse response filter (FIR) or infinite impulse response (IIR) filter can be applied intermittently or continuously to a raw data stream in the sensor (or in the receiver) in order to algorithmically smooth the data stream, which can produce a time lag (for example, as shown in measured glucose data 380 of FIG. 28). In some embodiments, wherein the analyte sensor is a subcutaneously implantable sensor, there may be a variable time lag associated with the tissue ingrowth at the biointerface at the tissue-device interface. Additionally, time lags can be variable upon a host's metabolism. In some embodiments, a time lag of the reference analyte data may be associated with an amount of time a user takes to test and report a reference analyte value. Accordingly, the preferred embodiments provide for estimation of analyte values based on measured analyte values, which can be used to compensate for a time lag such as described above, allow for output of analyte values that represent estimated present analyte values without a time lag.

Accordingly, some embodiments selectively apply estimative algorithms based on a measured, estimated, or predetermined time lag associated with the continuous analyte sensor. In some embodiments, estimative algorithms continuously run in order to continuously compensate for a time lag between reference and sensor data, such as described in more detail below. In some embodiments, estimative algorithms run during outlier detection in order to intelligently and dynamically match corresponding reference and sensor data for more accurate outlier inclusion or exclusion, such as described in more detail below. In some embodiments, estimative algorithms run during matching of data pairs for consideration in the calibration set in order to intelligently and dynamically match corresponding reference and sensor glucose data for better calibration, such as described in more detail below.

FIG. 27 is a flow chart that illustrates the process 370 of compensating for a time lag associated with a continuous analyte sensor to provide real-time estimated analyte data output in one embodiment. For the reasons described above, the system includes programming that continuously or periodically (e.g., when a user activates the LCD screen) compensates for a time lag in the system to provide a better real-time estimate to the user, for example.

At block 372, the time lag compensation process 370 obtains sensor data, which can be raw, smoothed, and/or otherwise processed. In some embodiments, estimation can be applied to a raw data stream received from an analyte sensor, such as described in more detail elsewhere herein. In some embodiments, estimation can be applied to calibrated data, such as described in more detail elsewhere herein.

At block 374, the time lag compensation process 370 continuously or periodically estimates analyte values for a present time period to compensate for a physiological or computational time lag in the sensor data stream. For example, if a 20-minute time lag is known inherent within the continuous analyte sensor, the compensation can be a 20-minute projected estimation to provide true present time (or “real time”) analyte values. Some embodiments can continuously run estimation to compensate for time lag, while other embodiments can perform time lag compensation estimation only when the user interface (e.g., LCD screen) is activated by a user. Known estimation algorithms and/or the dynamic and intelligent estimation algorithms of the preferred embodiments (e.g., such as described with reference to block 358 and FIGS. 30 to 40) can be used in estimating analyte values herein.

At block 376, the time lag compensation process 370 continuously or periodically provides output of the present time estimated analyte values, such as described in more detail above. Output can be sent to the user interface 160 or to an external device 180.

Referring now to FIG. 28, which is a graph that illustrates the data of FIG. 26, including reference analyte data, corresponding calibrated sensor analyte data, and corresponding estimated analyte data, showing compensation for time lag using estimation. The x-axis represents time in minutes and the y-axis represents glucose concentration in mg/dL. Reference glucose values 368 were obtained from the host from the reference glucose monitor during the 4-hour time period and correspond to FIG. 26. Measured glucose data 380 was obtained by smoothing and calibrating the raw data stream 366 of FIG. 26 using reference glucose values 368, such as described in more detail elsewhere herein. Estimated glucose data 382 was obtained by estimating using dynamic and intelligent estimation of the preferred embodiments, which is described in more detail below.

The measured glucose data 380 has been smoothed and thereby includes a data processing-related time lag, which may be in addition to physiological or membrane-related time lag, for example. Therefore, the measured glucose data 380 visibly lags behind the reference glucose values 368 on the graph. The estimated glucose data 382 includes dynamic and intelligent estimation of the preferred embodiments in order to compensate for the time lag, thereby better correlating with the reference glucose values 368. In this embodiment, the time lag compensation (estimation) is 15 minutes, however in other embodiments the time lag compensation (estimation) can be more or less.

In some embodiments, the estimation can be programmed to compensate for a predetermined time lag (for example, 0 to 60 minutes, or more). In some alternative embodiments, the estimation can be dynamically adjusted based on a measured time lag; for example, when estimation is used to dynamically match sensor analyte data with reference analyte data such as described below, the time difference between best corresponding sensor analyte data and reference analyte data can be used to determine the time lag.

FIG. 29 is a flow chart that illustrates the process 384 of matching data pairs from a continuous analyte sensor and a reference analyte sensor in one embodiment. Estimative algorithms of the preferred embodiments are useful when selectively applied during the process of matching corresponding sensor and reference analyte data, for example during outlier detection and/or matching data pairs for calibration, such as described in more detail elsewhere herein. For the reasons stated above with reference to FIGS. 26 to 28, for example, a time lag associated with the continuous analyte sensor and/or the reference analyte monitor can hinder the ability to accurately match data from the analyte sensor with corresponding data from the reference analyte monitor using time-correspondence only.

At block 386, the data matching process 384 obtains sensor data, which can be raw, smoothed, and/or otherwise processed. In some embodiments, data matching can use data from a raw data stream received from an analyte sensor, such as described in more detail elsewhere herein. In some embodiments, data matching can use calibrated data, such as described in more detail elsewhere herein.

At block 388, the data matching process 384, receives analyte values from a reference analyte monitor, including one or more reference glucose data points, hereinafter referred as “reference data” or “reference analyte data.” In an example wherein the analyte sensor is a continuous glucose sensor, the reference analyte monitor can be a self-monitoring blood glucose (SMBG) meter. Other examples are described in more detail elsewhere herein

At block 390, the data matching process 384 estimates one or more analyte values for a time period during which no data exists (or when data is unreliable or inaccurate, for example) based on the data stream. For example, the estimated analyte values can include values at intervals from about 30 seconds to about 5 minutes, and can be estimated for a time period of about 5 minutes to about 60 minutes in the future. In some embodiments, the time interval and/or time period can be more or less. Known estimation algorithms and/or the dynamic and intelligent estimation algorithms of the preferred embodiments (e.g., such as described with reference to block 358 and FIGS. 30 to 40) can be used in estimating analyte values herein.

At block 392, the data matching process 384 creates at least one matched data pair by matching reference analyte data to a corresponding analyte value from the one or more estimated analyte values. In some embodiments, the best matched pair can be evaluated by comparing a reference data point against individual sensor values over a predetermined time period (for example, +/−0 to 60 minutes). In one such embodiment, the reference data point is matched with sensor data points at intervals (for example, 5-minute intervals of measured historical analyte values and estimated future analyte values) and each matched pair is evaluated. The matched pair with the best correlation (for example, based on statistical deviation, clinical risk analysis, or the like) can be selected as the best matched pair and should be used for data processing. In some alternative embodiments, matching a reference data point with an average of a plurality of sensor data points over a time period can be used to form a matched pair.

Therefore, the preferred embodiments provide for estimation of analyte values based on measured analyte values that can be helpful in more accurately and/or appropriately matching sensor and reference analyte values that represent corresponding data. By increasing the accuracy of matched data pairs, true real-time estimated analyte values (for example, without a time lag) can be provided, calibration can be improved, and outlier detection can be more accurate and convenient, thereby improving overall patient safety and convenience.

While any of the above uses and applications can be applied using conventional algorithms that provide conventional projection based on first or second order regression, for example, it has been found that analyte value estimation can be further improved by adaptively applying algorithms, for example using dynamic intelligence such as described in more detail below. The dynamic and intelligent algorithms described herein can be applied to the uses and applications described above, or for estimating analyte values at any time for any use or application.

FIG. 30 is a flow chart that illustrates the dynamic and intelligent estimation algorithm selection process 396 in one embodiment.

At block 398, the dynamic and intelligent estimation algorithm selection process 396 obtains sensor data, which can be raw, smoothed, and/or otherwise processed. In some embodiments, data matching can use data from a raw data stream received from an analyte sensor, such as described in more detail elsewhere herein. In some embodiments, data matching can use calibrated data, such as described in more detail elsewhere herein.

At block 400, the dynamic and intelligent estimation algorithm selection process 396 includes selecting one or more algorithms from a plurality of algorithms that best fits the measured analyte values. In some embodiments, the estimative algorithm can be selected based on physiological parameters; for example, in an embodiment wherein the analyte sensor is a glucose sensor, a first order regression can be selected when the rate of change of the glucose concentration is high, indicating correlation with a straight line, while a second order regression can be selected when the rate of change of the glucose concentration is low, indicating correlation with a curved line. In some embodiments, a first order regression can be selected when the reference glucose data is within a certain threshold (for example, 100 to 200 mg/dL), indicating correlation with a straight line, while a second order regression can be selected when the reference glucose data is outside of a certain threshold (for example, 100 to 200 mg/dL), indicating correlation with a curved line because the likelihood of the glucose concentration turning around (for example, having a curvature) is greatest at high and low values.

Generally, algorithms that estimate analyte values from measured analyte values include any algorithm that fits the measured analyte values to a pattern, and/or extrapolates estimated values for another time period (for example, for a future time period or for a time period during which data needs to be replaced). In some embodiments, a polynomial regression (for example, first order, second order, third order, etc.) can be used to fit measured analyte values to a pattern, and then extrapolated. In some embodiments, autoregressive algorithms (for example, IIR filter) can be used to fit measured analyte values to a pattern, and then extrapolated. In some embodiments, measured analyte values can be filtered by frequency before projection (for example, by converting the analyte values with a Fourier transform, filtering out high frequency noise, and converting the frequency data back to time values by using an inverse Fourier transform); this data can then be projected forward (extrapolated) along lower frequencies. In some embodiments, measured analyte values can be represented with a Wavelet transform (for example filtering out specific noise depending on wavelet function), and then extrapolate forward. In some alternative embodiments, computational intelligence (for example, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, or the like) can be used to fit measured analyte values to a pattern, and/or extrapolate forward. In yet other alternative embodiments, time-series forecasting, using methods such as moving average (single or double), exponential smoothing (single, double, or triple), time series decomposition, growth curves, Box-Jenkins, or the like. The plurality of algorithms of the preferred embodiments can utilize any one or more of the above-described algorithms, or equivalents, in order to intelligently select estimative algorithms and thereby estimate analyte values.

In some embodiments, estimative algorithms further include parameters that consider external influences, such as insulin therapy, carbohydrate consumption, or the like. In one such example, these additional parameters can be user input via the user interface 160 or transmitted from an external device 180, such as described in more detail with reference to FIG. 17A. By including such external influences in additional to historical trend data (measured analyte values), analyte concentration changes can be better anticipated.

At block 402, the selected one or more algorithms are evaluated based on statistical, clinical, or physiological parameters. In some embodiments, running each algorithm on the data stream tests each of the one or more algorithms, and the algorithmic result with the best correlation to the measured analyte values is selected. In some embodiments, the pluralities of algorithms are each compared for best correlation with physiological parameters (for example, within known or expected rates of change, acceleration, concentration, etc). In some embodiments, the pluralities of algorithms are each compared for best fit within a clinical error grid (for example, within “A” region of Clarke Error Grid). Although first and second order algorithms are exemplified herein, any two or more algorithms such as described in more detail below could be programmed and selectively used based on a variety of conditions, including physiological, clinical, and/or statistical parameters.

At block 404, the algorithm(s) selected from the evaluation step is employed to estimate analyte values for a time period. Accordingly, analyte values are more dynamically and intelligently estimated to accommodate the dynamic nature of physiological data. Additional processes, for example applying physiological boundaries (FIG. 32), evaluation of the estimation algorithms after employing the algorithms (FIG. 34), evaluating a variation of estimated analyte values (FIG. 36), measuring and comparing analyte values (FIG. 39), or the like can be applied to the dynamic and intelligent estimative algorithms described with reference to FIG. 30.

FIG. 31 is a graph that illustrates dynamic and intelligent estimation algorithm selection applied to a data stream in one embodiment showing first order estimation, second order estimation, and the measured glucose values for the time period, wherein the second order estimation shows a better correlation to the measured glucose data than the first order estimation. The x-axis represents time in minutes. The y-axis represents glucose concentration in mg/dL.

In the data of FIG. 31, measured (calibrated) sensor glucose data 406 was obtained up to time t=0. At t=0, a first order regression 408 was performed on the measured data 406 to estimate the upcoming 15-minute time period. A second order regression 410 was also performed on the data to estimate the upcoming 15-minute time period. The intelligent estimation of the preferred embodiments, such as described in more detail below, chose the second order regression 410 as the preferred algorithm for estimation based on programmed conditions (at t=0). The graph of FIG. 31 further shows the measured glucose values 412 from t=0 to t=15 to illustrate that second order regression 410 does in fact more accurately correlate with the measured glucose data 412 than first order regression 408 from t=0 to t=15.

In the example of FIG. 31, the dynamic and intelligent estimation algorithm selection determined that the second order regression 410 was the preferred algorithm for estimation at t=0 based on conditions. A first condition was based on a set threshold that considers second order regression a better fit when measured glucose values are above 200 mg/dL and trending upwardly. A second condition verifies that the curvature of the second order regression line appropriately shows a deceleration above 200 mg/dL. Although two specific examples of conditions are described herein, dynamic and intelligent estimation can have as many or as few conditions programmed therein as can be imagined or contrived. Some additional examples of conditions for selecting from a plurality of algorithms are listed above, however the scope of this aspect of dynamic and intelligent estimation includes any conditional statements that can be programmed and applied to any algorithms that can be implemented for estimation.

FIG. 32 is a flow chart that illustrates the process 414 of estimating analyte values within physiological boundaries in one embodiment. The embodiment described herein is provided because the estimative algorithms such as described with reference to FIG. 30 consider mathematical equations, which may or may not be sufficient to accurately estimate analyte values based on measured analyte values.

At block 416, the analyte value estimation with physiological boundaries process 414 obtains sensor data, which can be raw, smoothed, calibrated and/or otherwise processed.

At block 418, the analyte value estimation with physiological boundaries process 414 estimates one or more analyte values using one or more estimation algorithms. In some embodiments, this analyte value estimation uses conventional projection using first or second order regression, for example. In some embodiments, dynamically and intelligently selecting of one or more algorithms from a plurality of algorithms (FIG. 30), evaluating estimation algorithms after employing the algorithms (FIG. 13), evaluating a variation of estimated analyte values (FIG. 36), measuring and comparing analyte values (FIG. 39), or the like can be applied to the dynamic and intelligent estimative algorithms described with reference to FIG. 30.

At block 420, the analyte value estimation with physiological boundaries process 414 applies physiological boundaries to the estimated analyte values of block 418. In some circumstances, physiological changes in a host and associated sensor data stream follow a relatively mathematical curvature. However there are additional considerations that are not inherently included in the mathematical calculation of estimative algorithms, such as physiological boundaries. One example of a circumstance or consideration that can occur is signal noise or signal artifact on the data stream, for example due to transient ischemia, signal from an interfering species, or the like. In such circumstances, normal mathematical calculations can result in estimated analyte values that fall outside of physiological boundaries. For example, a first order regression can produce a line that exceeds a known physiological rate of change of glucose in humans (for example, about 4 to 5 mg/dL/min). As another example, a second order regression can produce a curvature that exceeds a known physiological acceleration in humans (for example, about 0.1 to 0.2 mg/dL/min²). As yet another example, it has been observed that the best solution for the shape of the curve at any point along a glucose signal data stream over a certain time period (for example, about 20 to 30 minutes) is a straight line, which can be used to set physiological boundaries. As yet another example, a curvature defined by a second order regression at low glucose values (for example, below 80 mg/dL) generally decelerates as it goes down and accelerates as it goes up, while a curvature defined by a second order regression at high glucose values generally decelerates as it goes up and accelerates as it goes down. As yet another example, an individual's physiological patterns can be monitored over a time period (for example, from about one day to about one year) and individual's physiological patterns quantified using pattern recognition algorithms; the individual's physiological patterns could be used to increase the intelligence of the estimation by applying the quantified patterns to the estimated analyte values.

Accordingly, physiological boundaries, includes those described above, or other measured or known physiological analyte boundaries, can compliment an estimative algorithm to ensure that the estimated analyte values fall within known physiological parameters. However, in some alternative embodiments, physiological boundaries can be applied to raw and/or smoothed data, thereby eliminating the need for the estimation step (block 418).

FIG. 33 is a graph that illustrates physiological boundaries applied to a data stream in one embodiment, wherein the dynamic and intelligent estimation includes performing an estimative algorithm and further applies physiological boundaries to the estimated analyte data. The x-axis represents time in minutes. The y-axis represents glucose concentration in mg/dL. Measured glucose data 422 is shown for about 90 minutes up to t=0. At t=0, an estimative algorithm performs estimation using a second order regression of the previous 40 minutes to generate a slope and acceleration, which are used to extrapolate the estimated glucose data 424 beginning at the measured analyte data at t=0. At the same time (t=0), the system uses known physiological parameters to determine physiologically feasible boundaries of glucose concentration over the estimated 15-minute period. In this example, the system uses a slope and intercept defined by a first order regression using 25 minutes of data up to t=0, from which the system sets physiological boundaries using a maximum acceleration of glucose of 0.2 mg/dL/min² and a maximum rate of change of glucose of 4 mg/dL/min for the upcoming 15 minutes. Using the above-described physiological parameters, an upper physiological boundary 426 and a lower physiological boundary 428 are set. Interestingly, the estimated glucose data 424 falls outside of the physiological boundaries, namely above the upper physiological boundary 426. In this case, the second order regression estimated glucose data 424 has either a rate of change greater than 4 mg/dL/min and/or acceleration greater than 0.2 mg/dL/min². Such circumstances can be caused by noise on the signal, artifact of performing regression over a predetermined time period during which a change in analyte concentration is not best described by a regression line, or numerous other such affects.

In this case, estimated glucose values 424 can be adjusted to be the upper limit 426 in order to better represent physiologically feasible estimated analyte values. In some embodiments, some or all of the estimated analyte values falling outside of the physiological parameters can trigger the dynamic and intelligent estimative algorithms to re-select an algorithm, or to adjust the parameters of the algorithm (for example, increase and/or decrease the number of data points considered by the algorithm) to better estimate during that time period. In some alternative embodiments, statistical and or clinical boundaries can be used to bound estimated analyte values and/or adjust the parameters that drive those algorithms.

FIG. 34 is a flow chart that illustrates the process 430 of dynamic and intelligent estimation and evaluation of analyte values in one embodiment, wherein the estimation algorithms are continuously, periodically, or intermittently evaluated based on statistical, clinical, or physiological parameters to maintain accuracy of estimation.

At block 432, the dynamic and intelligent estimation and evaluation process 430 obtains sensor data, which can be raw, smoothed, calibrated and/or otherwise processed.

At block 434, the dynamic and intelligent estimation and evaluation process 430 estimates one or more analyte values using one or more estimation algorithms. In some embodiments, this analyte value estimation uses conventional projection using first or second order regression, for example. In some embodiments, dynamically and intelligently selecting of one or more algorithms from a plurality of algorithms (FIG. 30), dynamically and intelligently estimating analyte values within physiological boundaries (FIG. 32), evaluating a variation of estimated analyte values (FIG. 36), measuring and comparing analyte values (FIG. 39), or the like can be applied to the dynamic and intelligent estimation and evaluation process described herein with reference to FIG. 34.

The estimative algorithms described elsewhere herein consider mathematical equations (FIG. 30) and optionally physiological parameters (FIG. 32), which may or may not be sufficient to accurately estimate analyte values in some circumstances due to the dynamic nature of mammalian behavior. For example, in a circumstance where a patient's glucose concentration is trending upwardly at a constant rate of change (for example, 120 mg/dL at 2 mg/dL/min), an expected physiological pattern would likely estimate a continued increase at substantially the same rate of change over the upcoming approximately 40 minutes, which would fall within physiological boundaries. However, if a person with diabetes were to engage in heavy aerobic exercise, which may not be known by the estimative algorithm, a slowing of the upward trend, and possibly a change to a downward trend can possibly result, leading to inaccuracies in the estimated analyte values. Numerous such circumstances can occur in the lifestyle of a person with diabetes. However, although analyte values can sometimes be estimated under “normal” circumstances, other circumstances exist that are not “normal” or “expected” and can result in estimative algorithms that produce apparently erroneous results, for example, if they are based solely on mathematical calculations and/or physiological patterns. Accordingly, evaluation of the estimative algorithms can be performed to ensure the accuracy or quantify a measure of confidence in the estimative algorithms.

At block 436, the dynamic and intelligent estimation and evaluation process 430 evaluates the estimation algorithms employed at block 434 to evaluate a “goodness” of the estimated analyte values. The evaluation process performs an evaluation of the measured analyte data with the corresponding estimated analyte data (e.g., by performing the algorithm on the data stream and comparing the measured with the corresponding analyte data for a time period). In some embodiments, evaluation can be performed continually or continuously so that the dynamic and intelligent algorithms are continuously adapting to the changing physiological analyte data. In some embodiments, the evaluation can be performed periodically so that the dynamic and intelligent algorithms are periodically and systematically adapting to the changing physiological analyte data. In some embodiments, evaluation can be performed intermittently, for example when an estimative algorithm is initiated or other such triggers, so that the dynamic and intelligent algorithms can be evaluated when new or updated data or algorithms are being processed.

This evaluation process 430 uses any known evaluation method, for example based on statistical, clinical, or physiological standards. One example of statistical evaluation is provided below with reference to FIG. 35; however other methods are also possible. In some embodiments, the evaluation process 430 determines a correlation coefficient of regression. In some embodiments wherein the sensor is a glucose sensor, the evaluation process 430 determines if the selected estimative algorithm shows that analyte values fall with the “A” and “B” regions of the Clarke Error Grid. Other parameters or methods for evaluation are considered within the scope of the preferred embodiments. In some embodiments, the evaluation process 430 includes performing a curvature formula to determine fiducial information about the curvature, which results in an evaluation of the amount of noise on the signal.

In some embodiments, the evaluation process 430 calculates physiological boundaries to evaluate whether the estimated analyte values fall within known physiological constraints. This evaluation is particularly helpful when physiological constraints, such as described with reference to FIG. 32 above, have not been applied to the estimative algorithm. In this embodiment, the estimative algorithm(s) are evaluated to ensure that they do not allow estimated analyte values to fall outside of physiological boundaries, some examples of which are described in more detail with reference to FIG. 32 above, and in the definitions section, for example. In some alternative embodiments, clinical or statistical parameters can be used in a similar manner to bound estimated analyte values.

If the result of the evaluation is satisfactory (for example, 10% average deviation, correlation coefficient above 0.79, all estimated analyte values within A or B region of the Clarke Error Grid, all estimated analyte values within physiological boundaries, or the like), the processing continues to the next step, using the selected estimative algorithm. However, if the result of the evaluation is unsatisfactory, the process can start the algorithm selection process again, optionally considering additional information, or the processor can determine that estimation is not appropriate for a certain time period. In one alternative embodiment, a signal noise measurement can be evaluated, and if the signal to noise ratio is unacceptable, the processor can modify its estimative algorithm or other action that can help compensate for signal noise (e.g., signal artifacts, such as described in U.S. Pat. No. 6,931,327, which is incorporated herein by reference in its entirety).

FIG. 35 is a graph that illustrates an evaluation of the selected estimative algorithm in one embodiment, wherein a correlation is measured to determine a deviation of the measured glucose data with the selected estimative algorithm, if any. The x-axis represents time in minutes. The y-axis represents glucose concentration in mg/dL. Measured glucose values 440 are shown for about 90 minutes up to t=0. At t=0, the selected algorithm is performed on 40 minutes of the measured glucose values 440 up to t=0, which is represented by a regression line 442 in this embodiment. A data association function is used to determine a goodness of fit of the estimative algorithm on the measured glucose data 440; namely, the estimative algorithm is performed retrospectively on the measured glucose data 440, and is hereinafter referred to as retrospectively estimated glucose data 442 (e.g., estimation prior to t=0), after which a correlation (or deviation) with the measured glucose data is determined. In this example, the goodness of fit shows a mean absolute relative difference (MARD) of 3.3% between the measured glucose data 440 and the retrospectively estimated glucose data 442. While not wishing to be bound to theory, it is believed that this correlation of the measured glucose data 440 to the retrospectively estimated glucose data 442 can be indicative of the correlation of future estimated glucose data to the measured glucose data for that estimated time period.

Reference is now made to FIG. 36, which is a flow chart that illustrates the process 450 of analyzing a variation of estimated future analyte value possibilities in one embodiment. This embodiment takes into consideration that analyte values are subject to a variety of external influences, which can cause the measured analyte values to alter from the estimated analyte values as the time period that was estimated passes. External influences include, but are not limited to, exercise, sickness, consumption of food and alcohol, injections of insulin, other medications, or the like. For a person with diabetes, for example, even when estimation does not accurately predict the upcoming measured analyte values, the estimated analyte values can be valuable to a patient in treatment and in fact can even alter the estimated path by encouraging proactive patient behavior that can cause the patient to avoid the estimated clinical risk. In other words, the deviation of measured analyte values from their corresponding estimated analyte values may not be an “error” in the estimative algorithm, and is in fact one of the benefits of the continuous analyte sensor of the preferred embodiments, namely encouraging patient behavior modification and thereby improving patient health through minimizing clinically risky analyte values. Proactive behavior modification (for example, therapies such as insulin injections, carbohydrate consumption, exercise, or the like) can cause the patient's measured glucose to change from the estimated path, and analyzing a variation that can be associated with the estimated analyte values can encompass many of these changes. Therefore, in addition to estimated analyte values, a variation can be calculated or estimated based on statistical, clinical, and/or physiological parameters that provides a range of values in which the estimated analyte values can fall.

At block 452, the variation of possible estimated analyte values analysis process 450 obtains sensor data, which can be raw, smoothed, calibrated and/or otherwise processed.

At block 454, the variation of possible estimated analyte values analysis process 450 estimates one or more analyte values using one or more estimation algorithms. In some embodiments, this analyte values estimation uses conventional projection using first or second order regression, for example. In some embodiments, dynamically and intelligently selecting of one or more algorithms from a plurality of algorithms (FIG. 30), dynamically and intelligently estimating analyte values within physiological boundaries (FIG. 32), dynamic and intelligent estimation and evaluation of estimated analyte values (FIG. 34), measuring and comparing analyte values (FIG. 39), or the like can be applied to the dynamic and intelligent estimation and evaluation process described herein with reference to FIG. 36.

At block 456, the variation of possible estimated analyte values evaluation process 450 analyzes a variation of the estimated analyte data. Particularly, a statistical, clinical, and/or physiological variation of estimated analyte values can be calculated when applying the estimative algorithms and/or can be calculated at regular intervals to dynamically change as the measured analyte values are obtained. In general, analysis of trends and their variation allows the estimation of the preferred embodiments to dynamically and intelligently anticipate upcoming conditions, by considering internal and external influences that can affect analyte concentration.

In some embodiments, physiological boundaries for analytes in mammals can be used to set the boundaries of variation. For example, known physiological boundaries of glucose in humans are discussed in detail herein, with reference to FIG. 32, and in the definitions section, however any physiological parameters for any measured analyte could be implemented here to provide this variation of physiologically feasible analyte values.

In some embodiments, statistical variation can be used to determine a variation of possible analyte values. Statistical variation can be any known divergence or change from a point, line, or set of data based on statistical information. Statistical information includes patterns or data analysis resulting from experiments, published or unpublished, for example. In some embodiments, statistical information can include normal patterns that have been measured statistically in studies of analyte concentrations in mammals, for example. In some embodiments, statistical information can include errors observed and measured statistically in studies of analyte concentrations in mammals, for example. In some embodiments, statistical information can include predetermined statistical standards, for example, deviation less than or equal to 5% on the analyte value. In some embodiments, statistical variation can be a measured or otherwise known signal noise level.

In some embodiments, a variation is determined based on the fact that the conventional blood glucose meters are known to have up to a +/−20% error in glucose values (namely, on average in the hands of a patient). For example, gross errors in glucose readings are known to occur due to patient error in self-administration of the blood glucose test. In one such example, if the user has traces of sugar on his/her finger while obtaining a blood sample for a glucose concentration test, then the measured glucose value will likely be much higher than the measured glucose value in the blood. Additionally, it is known that self-monitored blood glucose tests (for example, test strips) are occasionally subject to manufacturing error. In view of this statistical information, in an embodiment wherein a continuous glucose sensor relies upon a conventional blood glucose meter for calibration, this +/−20% error should be considered because of the potential for translated effect on the calibrated sensor analyte data. Accordingly, this exemplary embodiment would provide for a +/−20% variation of estimated glucose values based on the above-described statistical information.

In some embodiments, a variation of estimated analyte values can be analyzed based on individual physiological patterns. Physiological patterns are affected by a combination of at least biological mechanisms, physiological boundaries, and external influences such as exercise, sickness, consumption of food and alcohol, injections of insulin, other medications, or the like. Advantageously, pattern recognition can be used with continuous analyte sensors to characterize an individual's physiology; for example the metabolism of a person with diabetes can be individually characterized, which has been difficult to quantify with conventional glucose sensing mechanisms due to the unique nature of an individual's metabolism. Additionally, this information can be advantageously linked with external influences (for example, patient behavior) to better understand the nature of individual human physiology, which can be helpful in controlling the basal rate in a person with diabetes, for example.

While not wishing to be bound to theory, it is believed that monitoring of individual historical physiological analyte data can be used to recognize patterns that can be used to estimate analyte values, or ranges of values, in a mammal. For example, measured analyte data for a patient can show certain peaks of glucose levels during a specific time of day, “normal” AM and PM eating behaviors (for example, that follow a pattern), weekday versus weekend glucose patterns, individual maximum rate of change, or the like, that can be quantified using patient-dependent pattern recognition algorithms, for example. Pattern recognition algorithms that can be used in this embodiment include, but are not limited to, stochastic nonlinear time-series analysis, exponential (non-linear) autoregressive model, process feedback nonlinear autoregressive (PFNAR) model, neural networks, or the like.

Accordingly, statistically calculated patterns can provide information useful in analyzing a variation of estimated analyte values for a patient that includes consideration of the patient's normal physiological patterns. Pattern recognition enables the algorithmic analysis of analyte data to be customized to a user, which is useful when analyte information is variable with each individual user, such as has been seen in glucose in humans, for example.

In some embodiments, a variation of estimated analyte values is on clinical risk analysis. Estimated analyte values can have higher clinical risk in certain ranges of analyte values, for example analyte values that are in a clinically risky zone or analyte values that are changing at a clinically risky rate of change. When a measured analyte value or an estimated analyte value shows existing or approaching clinical risk, it can be important to analyze the variation of estimated analyte values in view of the clinical risk to the patient. For example, in an effort to aid a person with diabetes in avoiding clinically risky hyper- or hypoglycemia, a variation can be weighted toward the clinically risk zone, which can be used to emphasize the pending danger to the patient, doctor, or care taker, for example. As another example, the variation of measured or estimated analyte values can be based on values that fall within the “A” and/or “B” regions of an error grid Analysis Method.

In case of variation analysis based on clinical risk, the estimated analyte values are weighted in view of pending clinical risk. For example, if estimated glucose values show a trend toward hypoglycemia at a certain rate of change, a variation of possible trends toward hypoglycemia are weighted to show how quickly the glucose concentration could reach 40 mg/dL, for example. As another example, if estimated glucose values show a trend toward hyperglycemia at a certain acceleration, a variation of possible trends toward hyperglycemia are weighted to show how quickly the glucose concentration could reach 200 mg/dL, for example.

In some embodiments, when a variation of the estimated analyte values shows higher clinical risk as a possible path within that variation analysis as compared to the estimated analyte path, the estimated analyte values can be adjusted to show the analyte values with the most clinical risk to a patient. While not wishing to be bound by theory, adjusting the estimated analyte values for the highest variation of clinical risk exploits the belief that by showing the patient the “worst case scenario,” the patient is more likely to address the clinical risk and make timely behavioral and therapeutic modifications and/or decisions that will slow or reverse the approaching clinical risk.

At block 458, the variation of possible estimated analyte values evaluation process 450 provides output based on the variation analysis. In some embodiments, the result of this variation analysis provides a “zone” of possible values, which can be displayed to the user, considered in data analysis, and/or used in evaluating of performance of the estimation, for example. A few examples of variation analysis display are shown in FIGS. 45 to 47; however other methods of formatting or displaying variation analysis data are contemplated within the scope of the invention.

FIG. 37 is a graph that illustrates variation analysis of estimated glucose values in one embodiment, wherein a variation of the estimated glucose values is analyzed and determined based on known physiological parameters. The x-axis represents time in minutes. The y-axis represents glucose concentration in mg/dL. In this embodiment, the known maximum rate of change and acceleration of glucose in humans are used to provide the variation about the estimated glucose path.

The measured glucose values 460 are shown for about 90 minutes up to t=0. At t=0, intelligent and dynamic estimation of the preferred embodiments is performed to obtain estimated glucose values 462. A variation of estimated glucose values is then determined based on physiological parameters, including an upper limit 464 and a lower limit 466 of variation defined by known physiological parameters, including rate of change and acceleration of glucose concentration in humans.

FIG. 38 is a graph that illustrates variation of estimated analyte values in another embodiment, wherein the variation is based on statistical parameters. The x-axis represents time in minutes and the y-axis represents glucose concentration in mg/dL. The measured glucose values 470 are shown for about 160 minutes up to t=0. At t=0, intelligent and dynamic estimation of the preferred embodiments is employed to obtain estimated glucose values 472. A variation is defined by upper and lower limits 474 that were determined using 95% confidence intervals. Bremer, T.; Gough, D. A. “Is blood glucose predictable from previous values? A solicitation for data.” Diabetes 1999, 48, 445-451, which is incorporated by reference herein in its entirety, teaches a method of determining a confidence interval in one embodiment.

Although some embodiments have been described for a glucose sensor, any measured analyte pattern, data analysis resulting from an experiment, or otherwise known statistical information, whether official or unofficial, published or unpublished, proven or anecdotal, or the like, can be used to provide the statistical variation described herein.

FIG. 39 is a flow chart that illustrates the process 480 of estimating, measuring, and comparing analyte values in one embodiment.

At block 482, the estimating, measuring, and comparing analyte values process 480 obtains sensor data, which can be raw, smoothed, calibrated and/or otherwise processed.

At block 484, the estimating, measuring, and comparing analyte values process 480 estimates one or more analyte values for a time period. In some embodiments, this analyte values estimation uses conventional projection using first or second order regression, for example. In some embodiments, dynamically and intelligently selecting of one or more algorithms from a plurality of algorithms (FIG. 30), dynamically and intelligently estimating analyte values within physiological boundaries (FIG. 32), dynamic and intelligent estimation and evaluation of estimated analyte values (FIG. 34), variation analysis (FIG. 36), or the like can be applied to the process described herein with reference to FIG. 39.

At block 486, the estimating, measuring, and comparing analyte values process 480 obtains sensor data for the time period for which the estimated analyte values were calculated at block 484. In some embodiments, the measured analyte data can be raw, smoothed, calibrated and/or otherwise processed.

At block 488, the estimating, measuring, and comparing analyte values process 480 compares the estimated analyte data to the measured analyte data for that estimated time period. In general, it can be useful to compare the estimated analyte data to the measured analyte data for that estimated time period after estimation of analyte values. This comparison can be performed continuously, namely, at regular intervals as data streams are processed into measured analyte values. Alternatively, this comparison can be performed based on events, such as during estimation of measured analyte values, selection of a estimative algorithm, evaluation of estimative algorithms, variation analysis of estimated analyte values, calibration and transformation of sensor analyte data, or the like.

One embodiment is shown in FIG. 40, wherein MARD is used to determine a correlation (or deviation), if any, between the estimated and measured data sets. In other embodiments, other methods, such as linear regression, non-linear mapping/regression, rank (for example, non-parametric) correlation, least mean square fit, mean absolute deviation (MAD), or the like, can be used to compare the estimated analyte data to the measured analyte data to determine a correlation (or deviation), if any.

In one embodiment, wherein estimation is used in outlier detection and/or in matching data pairs for a continuous glucose sensor (see FIGS. 27 and 28), the estimated glucose data can be plotted against reference glucose data on a clinical error grid (for example, Clarke Error Grid or rate grid) and then compared to the measured glucose data for that estimated time period plotted against the same reference analyte data on the same clinical error grid. In alternative embodiments, other clinical error analysis methods can be used, such as Consensus Error Grid, rate of change calculation, consensus grid, and standard clinical acceptance tests, for example. The deviation can be quantified by percent deviation, or can be classified as pass/fail, for example.

In some embodiments, the results of the comparison provide a quantitative deviation value, which can be used to provide a statistical variation; for example, if the % deviation is calculated as 8%, then the statistical variation such as described with reference to FIG. 36 can be updated with a +/−8% variation. In some alternative embodiments, the results of the comparison can be used to turn on/off the estimative algorithms, estimative output, or the like. In general, the comparison produces a confidence interval (for example, +/−8% of estimated values) which can be used in data analysis, output of data to a user, or the like.

A resulting deviation from this comparison between estimated and corresponding measured analyte values may or may not imply error in the estimative algorithms. While not wishing to be bound by theory, it is believed that the deviation between estimated and corresponding measured analyte values is due, at least in part, to behavioral changes by a patient, who observes estimated analyte values and determines to change the present trend of analyte values by behavioral and/or therapeutic changes (for example, medication, carbohydrate consumption, exercise, rest, or the like). Accordingly, the deviation can also be used to illustrate positive changes resulting from the educational aspect of providing estimated analyte values to the user, which is described in more detail with reference to FIGS. 31 to 37.

FIG. 40 is a graph that illustrates comparison of estimated analyte values in one embodiment, wherein previously estimated analyte values are compared to time corresponding measured analyte values to determine a correlation (or deviation), if any. The x-axis represents time in minutes. The y-axis represents glucose concentration in mg/dL. The measured glucose values 492 are shown for about 105 minutes up to t=15. The estimated analyte values 494, which were estimated at t=0 for 15 minutes, are shown superimposed over the measured analyte values 492. Using a 3-point MARD for t=0 to t=15, the estimated analyte values 494 can be compared with the measured analyte values 492 to determine a 0.55% average deviation.

Input and Output

In general, the above-described estimative algorithms, including estimation of measured analyte values and variation analysis of the estimated analyte values are useful when provided to a patient, doctor, family member, or the like. Even more, the estimative algorithms are useful when they are able to provide information helpful in modifying a patient's behavior so that they experience less clinically risky situations and higher quality of life than may otherwise be possible. Therefore, the above-described data analysis can be output in a variety of forms useful in caring for the health of a patient.

Output can be provided via a user interface, including but not limited to, visually on a screen, audibly through a speaker, or tactilely through a vibrator. Additionally, output can be provided via wired or wireless connection to an external device, including but not limited to, computer, laptop, server, personal digital assistant, modem connection, insulin delivery mechanism, medical device, or other device that can be useful in interfacing with the receiver.

Output can be continuously provided, or certain output can be selectively provided based on events, analyte concentrations or the like. For example, an estimated analyte path can be continuously provided to a patient on an LCD screen, while audible alerts can be provided only during a time of existing or approaching clinical risk to a patient. As another example, estimation can be provided based on event triggers (for example, when an analyte concentration is nearing or entering a clinically risky zone). As yet another example, analyzed deviation of estimated analyte values can be provided when a predetermined level of variation (for example, due to known error or clinical risk) is known.

In contrast to alarms that prompt or alert a patient when a measured or projected analyte value or rate of change simply passes a predetermined threshold, the clinical risk alarms of the preferred embodiments combine intelligent and dynamic estimative algorithms to provide greater accuracy, more timeliness in pending danger, avoidance of false alarms, and less annoyance for the patient. In general, clinical risk alarms of the preferred embodiments include dynamic and intelligent estimative algorithms based on analyte value, rate of change, acceleration, clinical risk, statistical probabilities, known physiological constraints, and/or individual physiological patterns, thereby providing more appropriate, clinically safe, and patient-friendly alarms.

In some embodiments, clinical risk alarms can be activated for a predetermined time period to allow for the user to attend to his/her condition. Additionally, the clinical risk alarms can be de-activated when leaving a clinical risk zone so as not to annoy the patient by repeated clinical risk alarms, when the patient's condition is improving.

In some embodiments, the dynamic and intelligent estimation of the preferred embodiments determines a possibility of the patient avoiding clinical risk, based on the analyte concentration, the rate of change, and other aspects of the dynamic and intelligent estimative algorithms of the preferred embodiments. If there is minimal or no possibility of avoiding the clinical risk, a clinical risk alarm will be triggered. However, if there is a possibility of avoiding the clinical risk, the system can wait a predetermined amount of time and re-analyze the possibility of avoiding the clinical risk. In some embodiments, when there is a possibility of avoiding the clinical risk, the system will further provide targets, therapy recommendations, or other information that can aid the patient in proactively avoiding the clinical risk.

In some embodiments, a variety of different display methods are used, such as described in the preferred embodiments, which can be toggled through or selectively displayed to the user based on conditions or by selecting a button, for example. As one example, a simple screen can be normally shown that provides an overview of analyte data, for example present analyte value and directional trend. More complex screens can then be selected when a user desired more detailed information, for example, historical analyte data, alarms, clinical risk zones, or the like.

FIG. 41 is an illustration of the receiver in one embodiment showing an analyte trend graph, including measured analyte values, estimated analyte values, and a clinical risk zone. The receiver 158 includes an LCD screen 170, buttons 172, and a speaker 164 and/or microphone. The screen 170 displays a trend graph in the form of a line representing the historical trend of a patient's analyte concentration. Although axes may or may not be shown on the screen 170, it is understood that a theoretical x-axis represents time and a theoretical y-axis represents analyte concentration.

In some embodiments such as shown in FIG. 41, the screen shows thresholds, including a high threshold 500 and a low threshold 502, which represent boundaries between clinically safe and clinically risky conditions for the patients. In one exemplary embodiment, a normal glucose threshold for a glucose sensor is set between about 100 and 160 mg/dL, and the clinical risk zones 504 are illustrated outside of these thresholds. In alternative embodiments, the normal glucose threshold is between about 80 and about 200 mg/dL, between about 55 and about 220 mg/dL, or other threshold that can be set by the manufacturer, physician, patient, computer program, or the like. Although a few examples of glucose thresholds are given for a glucose sensor, the setting of any analyte threshold is not limited by the preferred embodiments.

In some embodiments, the screen 170 shows clinical risk zones 504, also referred to as danger zones, through shading, gradients, or other graphical illustrations that indicate areas of increasing clinical risk. Clinical risk zones 504 can be set by a manufacturer, customized by a doctor, and/or set by a user via buttons 172, for example. In some embodiments, the danger zone 504 can be continuously shown on the screen 170, or the danger zone can appear when the measured and/or estimated analyte values fall into the danger zone 504. Additional information that can be displayed on the screen includes, e.g., an estimated time to clinical risk. In some embodiments, the danger zone can be divided into levels of danger (for example, low, medium, and high) and/or can be color-coded (for example, yellow, orange, and red) or otherwise illustrated to indicate the level of danger to the patient. Additionally, the screen or portion of the screen can dynamically change colors or illustrations that represent a nearness to the clinical risk and/or a severity of clinical risk.

In some embodiments, such as shown in FIG. 41, the screen 170 displays a trend graph of measured analyte data 506. Measured analyte data can be smoothed and calibrated such as described in more detail elsewhere herein. Measured analyte data can be displayed for a certain time period (for example, previous 1 hour, 3 hours, 9 hours, etc.) In some embodiments, the user can toggle through screens using buttons 172 to view the measured analyte data for different time periods, using different formats, or to view certain analyte values (for example, highs and lows).

In some embodiments such as shown in FIG. 41, the screen 170 displays estimated analyte data 508 using dots. In this illustration, the size of the dots can represent the confidence of the estimation, a variation of estimated values, or the like. For example, as the time gets farther away from the present (t=0) the confidence level in the accuracy of the estimation can decline as is appreciated by one skilled in the art. In some alternative embodiments, dashed lines, symbols, icons, or the like can be used to represent the estimated analyte values. In some alternative embodiments, shaded regions, colors, patterns, or the like can also be used to represent the estimated analyte values, a confidence in those values, and/or a variation of those values, such as described in more detail in preferred embodiments.

Axes, including time and analyte concentration values, can be provided on the screen, however are not required. While not wishing to be bound by theory, it is believed that trend information, thresholds, and danger zones provide sufficient information to represent analyte concentration and clinically educate the user. In some embodiments, time can be represented by symbols, such as a sun and moon to represent day and night. In some embodiments, the present or most recent measured analyte concentration, from the continuous sensor and/or from the reference analyte monitor can be continually, intermittently, or selectively displayed on the screen.

The estimated analyte values 508 of FIG. 41 include a portion, which extends into the danger zone 504. By providing data in a format that emphasizes the possibility of clinical risk to the patient, appropriate action can be taken by the user (for example, patient or caretaker) and clinical risk can be preempted.

FIG. 42 is an illustration of the receiver in another embodiment showing a representation of analyte concentration and directional trend using a gradient bar. In this embodiment, the screen illustrates the measured analyte values and estimated analyte values in a simple but effective manner that communicates valuable analyte information to the user.

In this embodiment, a gradient bar 510 is provided that includes thresholds 512 set at high and lows such as described in more detail with reference to FIG. 41, above. Additionally, colors, shading, or other graphical illustration can be present to represent danger zones 514 on the gradient bar 510 such as described in more detail with reference to FIG. 41, above.

The measured analyte value is represented on the gradient bar 510 by a marker 516, such as a darkened or colored bar. By representing the measured analyte value with a bar 516, a low-resolution analyte value is presented to the user (for example, within a range of values). For example, each segment on the gradient bar 510 can represent about 10 mg/dL of glucose concentration. As another example, each segment can dynamically represent the range of values that fall within the “A” and “B” regions of the Clarke Error Grid. While not wishing to be bound by theory, it is believe that inaccuracies known both in reference analyte monitors and/or continuous analyte sensors are likely due to known variables such as described in more detail elsewhere herein, and can be de-emphasized such that a user focuses on proactive care of the condition, rather than inconsequential discrepancies within and between reference analyte monitors and continuous analyte sensors.

Additionally, the representative gradient bar communicates the directional trend of the analyte concentration to the user in a simple and effective manner, namely by a directional arrow 518. For example, in conventional diabetic blood glucose monitoring, a person with diabetes obtains a blood sample and measures the glucose concentration using a test strip, or the like. Unfortunately, this information does not tell the person with diabetes whether the blood glucose concentration is rising or falling. Rising or falling directional trend information can be particularly important in a situation such as illustrated in FIG. 42, wherein if the user does not know that the glucose concentration is rising, he/she may assume that the glucose concentration is falling and not attend to his/her condition. However, because rising directional trend information 518 is provided, the person with diabetes can preempt the clinical risk by attending to his/her condition (for example, administer insulin). Estimated analyte data can be incorporated into the directional trend information by characteristics of the arrow, for example, size, color, flash speed, or the like.

In some embodiments, the gradient bar can be a vertical instead of horizontal bar. In some embodiments, a gradient fill can be used to represent analyte concentration, variation, or clinical risk, for example. In some embodiments, the bar graph includes color, for example the center can be green in the safe zone that graduates to red in the danger zones; this can be in addition to or in place of the divided segments. In some embodiments, the segments of the bar graph are clearly divided by lines; however color, gradation, or the like can be used to represent areas of the bar graph. In some embodiments, the directional arrow can be represented by a cascading level of arrows to a represent slow or rapid rate of change. In some embodiments, the directional arrow can be flashing to represent movement or pending danger.

The screen 170 of FIG. 42 can further comprise a numerical representation of analyte concentration, date, time, or other information to be communicated to the patient. However, a user can advantageously extrapolate information helpful for his/her condition using the simple and effective representation of this embodiment shown in FIG. 42, without reading a numeric representation of his/her analyte concentration.

In some alternative embodiments, a trend graph or gradient bar, a dial, pie chart, or other visual representation can provide analyte data using shading, colors, patterns, icons, animation, or the like.

FIG. 43 is an illustration of a receiver in one embodiment, which includes measured analyte values and a target analyte value(s). FIG. 44 is an illustration of the receiver of 22 further including estimated analyte values. FIG. 45 is an illustration of the receiver of 23 further including variations of estimated analyte values and including therapy recommendations to aid a user in obtaining the target analyte value.

FIG. 43 is an illustration of the receiver 158 in one embodiment, wherein the screen 170 shows measured analyte values 520 and one (or more) clinically acceptable target analyte values 522. The measured analyte values 520 are illustrated as a trend graph, such as described with reference to FIG. 41, however other representations are also possible.

Additionally, one or more clinically acceptable target analyte values 522 are provided as output, for example such as shown in FIG. 43. In some embodiments, the clinically acceptable target analyte values can be obtained from a variation analysis of clinical, physiological, or statistical variation, such as described in more detail elsewhere herein. Namely, the variation analysis provides the analyzed variation of the estimated analyte values, and the output module 18 (or processor 16) further analyzes the variation of estimated analyte values for those that are clinically acceptable and optionally also ensures physiological feasibility. For example, analysis of clinical risk can visually direct a patient to aim for an analyte value in a safe zone (for example, outside of the clinically risky zone).

In some embodiments, the output displays a point representing a target analyte value. In some embodiments, the output displays an object representing a general target analyte area. In some embodiments, the output displays a path of target analyte values. In some embodiments, the output displays a range of target analyte values along that path.

Humans are generally particularly responsive to targets, namely, able to understand the intention of targets and desire to obtain them. Advantageously, the output of target analyte values provides a goal towards which the user will aim. In the example shown on FIG. 41, the measured analyte values 520 indicate an upward trend of analyte concentration, and a user can likely visualize that the trend of the measured analyte values 520 will not likely hit the target 522 without intervention or action. Therefore, a user will be prompted to proactively care for his/her analyte concentration in an effort to hit the target analyte value(s) 522 (for example, administer insulin).

In some embodiments, the manufacturer, physician, patient, computer program, or the like can set the target analyte values. In some embodiments, a physician can set static target analyte values based on age, time of day, meal time, severity of medical condition, or the like; in such embodiments, the targets can be regularly or intermittently displayed in an effort to modify patient behavior through habitual reminders and training. Targets can be continually maintained on the screen or selectively displayed, for example when clinical risk is estimated, but can be avoided. In some embodiments, the target values can be dynamic targets, namely, targets that are dependent upon variable parameters such as age, time of day, meal time, severity of medical condition, medications received (for example, insulin injections) or the like, which can be input by a user or external device.

In one example of targets useful for a person with diabetes monitoring glucose concentration, the target glucose levels for a person with diabetes are typically between about 80 and about 130 mg/dL before meals and less than about 180 mg/dL one to two hours after a meal. In another exemplary embodiment, the amount and timing of insulin injections can be considered in determining the estimation of and target glucose ranges for a person with diabetes.

FIG. 44 is an illustration of the receiver 158 in another embodiment showing the measured analyte values 520 and clinically acceptable target analyte value(s) 522 of FIG. 43 and further showing estimated analyte values 524 on the same screen. In some embodiments, the data can be separated onto different screens that can be selectively viewed. However, viewing both estimated analyte values and the target analyte values can be useful in educating the patient regarding control of his/her analyte levels, since estimated and target analyte values are physiologically feasible in view of known physiological parameters described elsewhere herein. Estimated analyte values can be calculated and displayed in any manner described in the preferred embodiments.

FIG. 45 is an illustration of a receiver in another embodiment, including measured analyte values 520, target analyte values 522, estimated analyte values 524, such as described in more detail above with reference to FIGS. 43 and 44, and further including variations of estimated analyte values 526 and therapy recommendations 528 on the screen to help the user obtain the displayed target analyte values 522. The variations of estimated analyte values are calculated such as described in more detail with reference to FIG. 36.

The target analyte values presented should be physiologically feasible; therefore, type and/or amount of therapy can be determined (or estimated) to aid the patient in obtaining those therapy goals. In some embodiments, the therapy recommendations are representative icons, such as the injection icon 528 shown in FIG. 45. In alternative embodiments, icons can include an apple, orange juice, candy bar, or any icon representative of eating, drinking, or medicating, for example. In some embodiments, the therapy recommendations are preset alphanumeric messages (for example, “consume carbohydrates”, “inject insulin”, or “no therapy required”). In some embodiments therapy recommendations can be customized (for example, by a manufacturer, physician, patient, computer program, and/or the like) in order to provide more reliable, accurate, clinically safe, and/or individualized goals. For example, a physician can input information helpful in determining therapy recommendations using individual physiological considerations. As another example, data can be input via the user interface or via a wired or wireless connection to the receiver, such as age, time of day, meal time, severity of medical condition, medications received (for example, insulin injections) or the like, which can be used to determine the appropriate therapy recommendations.

In some embodiments, the therapy recommendations include a variety of scenarios, which the viewer can view and/or select. In these embodiments, the patient is given more control and able to make decisions based that fits best with their lifestyle or present circumstance, or considering external influences of which the system was unaware.

In some embodiments, therapy recommendations are sent to an external device (for example, insulin delivery mechanism), which is described in more detail with reference to FIGS. 48 to 51.

FIGS. 46 and 47 are views of the receiver showing an analyte trend graph, including measured analyte values and dynamic visual representation of range of estimated analyte values based on a variation analysis, such as described in more detail with reference to FIG. 36.

FIG. 46 is an illustration of a receiver 158 in another embodiment, including a screen 170 that shows the measured analyte values 530 and a variation of estimated analyte values 532 in one exemplary embodiment. In this embodiment, the visual representation of the variation of estimated analyte values 532 includes exemplary paths representative of the analyzed variation of estimated analyte values that illustrates a range of possible future analyte values. In some embodiments, the variation of estimated analyte values 532 is represented by a shape that begins at the most recently measured analyte value 534 and includes boundaries 536 that represent the range of possible variations of estimated analyte values for a future time period. The shape can be static or dynamic depending on the type of variation analyzed by the estimative algorithm, for example a fan, teardrop, or other shaped object.

FIG. 47 is an illustration of a receiver 158 in another embodiment, including a screen 170 that shows the measured analyte values 538 and a variation of estimated analyte values 540 in another exemplary embodiment. In this embodiment, the variation can include an estimated path and boundaries, for example, which can be obtained from a variation analysis and/or from physiological parameters, for example. In some alternative embodiments, color or other illustrative representation of levels of safety or danger can be provided on the screen.

FIG. 48 is an illustration of a receiver 158 in another embodiment, including a screen 170 that shows a numerical representation of the most recent measured analyte value 542. This numerical value 542 is preferably a calibrated analyte value, such as described in more detail elsewhere herein. Additionally, this embodiment preferably provides an arrow 544 on the screen 170, which represents the rate of change of the host's analyte concentration. A bold “up” arrow is shown on the drawing, which preferably represents a relatively quickly increasing rate of change. The arrows shown with dotted lines illustrate examples of other directional arrows (for example, rotated by 45 degrees), which can be useful on the screen to represent various other positive and negative rates of change. Although the directional arrows shown have a relative low resolution (45 degrees of accuracy), other arrows can be rotated with a high resolution of accuracy (for example one degree of accuracy) to more accurately represent the rate of change of the host's analyte concentration. In some alternative embodiments, the screen provides an indication of the acceleration of the host's analyte concentration.

A second numerical value 546 is shown, which is representative of a variation of the measured analyte value 542. The second numerical value is preferable determined from a variation analysis based on statistical, clinical, or physiological parameters, such as described in more detail elsewhere herein. In one embodiment, the second numerical value 546 is determined based on clinical risk (for example, weighted for the greatest possible clinical risk to a patient). In another embodiment, the second numerical representation 546 is an estimated analyte value extrapolated to compensate for a time lag, such as described in more detail elsewhere herein. In some alternative embodiments, the receiver displays a range of numerical analyte values that best represents the host's estimated analyte value (for example, +/−10%). In some embodiments, the range is weighted based on clinical risk to the patient. In some embodiments, the range is representative of a confidence in the estimated analyte value and/or a variation of those values. In some embodiments, the range is adjustable.

Patient Display

The potential of continuous glucose monitoring as an aid to both diabetic patients and their caregivers is well recognized. For the patient, continuous monitoring provides hour-to-hour glucose information that enables intensive therapy: it can be used to reduce the extent of hyperglycemic excursions without increasing the risk of hypoglycemic events. For caregivers of patients with diabetes, continuous monitoring provides day-to-day glucose information that can be used to optimize therapy. Despite these differences in purpose/perspective (hour-to-hour data for the patient, day-to-day information for the caregiver), the conventional display of continuous glucose data has heretofore not been adapted to the intended use/user. Accordingly, continuous glucose display methods that are utility-driven, and that allow the data to be easily perceived and interpreted is desirable.

Glucose data are typically displayed on a graph with y-axis that spans a physiologic range of glucose (e.g. 40-400 mg/dl) and is uniform, i.e. the distance on the graph between 60 and 80 mg/dl is the same as the distance between 160 and 180 mg/dl, even though the clinical meanings of these two differences are significantly different. An alternative display uses a non-uniform y-axis that makes differences at low glucose levels easier to perceive. The difference in appearance of these two graphs is depicted in FIG. 49, which illustrates the conventional display of a 9-hour trend graph; FIG. 50 illustrates a display with a y-axis that has been equally divided into three zones (low, medium, and high glucose) though the glucose range (max−min) of each zone is different (40-90 mg/dl, 90-180 mg/dl, 180-400 mg/dl). The non-uniform y-axis in FIG. 50 appears to cause distortion to the glucose trend but does not appear to be misleading. More importantly, the dynamics at low glucose are more easily perceived in FIG. 50 than in FIG. 49.

Physicians use continuous glucose monitoring primarily for therapy optimization. Though the hour-to-hour dynamics of glucose can contain information related to therapy adjustment, a longer-term/summary perspective is perhaps easier perceive and interpret, and more reflective of changes in a patient's glycemic control. In this way, physician monitoring of a patient's glycemic control is similar to process monitoring used in quality control of manufactured products: the aim of both is to rapidly detect when the system/process is in or out of control, or to detect trends that can indicate changes in control. Control charts, which plot averages and ranges of process parameters over time, are a well-established and powerful illustration of process control and can be applicable to continuous glucose monitoring. FIGS. 51 and 52 illustrate the difference in how well the data reflect changes in glycemic control. FIG. 51 is a conventional plot of glucose over one week; FIG. 52 is a plot of the 24-hour (12 AM-12 AM) median (+/−interquartile range) glucose.

The display provides improved utility of continuous glucose data, enabling improved clinical outcomes, and offers advantages over prior art displays wherein the display of continuous glucose data is not tailored to the intended use.

FIG. 53 is an illustration of a receiver that interfaces with a computer. A receiver 158 is provided that is capable of communication with a computer 580. The communication can include one-way or two-way wired or wireless transmissions 582. The computer 580 can be any system that processes information, such as a PC, server, personal digital assistant (PDA), or the like.

In some embodiments, the receiver sends information to the computer, for example, measured analyte data, estimated analyte data, target analyte data, therapy recommendations, or the like. The computer can include software that processes the data in any manner known in the art.

In some embodiments, the computer sends information to the receiver; for example, updating software, customizing the receiver programming (for example, setting individualized parameters), providing real time information (for example, mealtime and exercise that has been entered into a PDA), or the like.

FIG. 54 is an illustration of a receiver 158 that interfaces with a modem 590, wherein data is transmitted via wireless transmissions 592 between the receiver and a modem in order to interface with a telecommunications line (for example, phone, pager, internet, network, etc). By providing an interface with a telecommunications line, the receiver can send and receive information from parties remote from the receiver, such as at a hospital, doctor's office, caretaker's computer, nationally-based server, or the like.

In some embodiments, the modem allows the receiver to send emergency messages to an emergency contact, such as a family member, hospital, Public Safety Answering Point (PSAP), or the like when analyte concentration are in a zone of extreme clinical risk. In some embodiments, a patient's doctor monitors his/her analyte concentration remotely and is able to request an appointment when certain conditions are not being met with the patient's analyte concentration. Numerous other uses can be contrived for communicating information via a modem 590 between the receiver 158 and another party, all of which are encompassed in the preferred embodiments.

FIG. 55 is an illustration of a receiver 158 that interfaces with an insulin pen 600, wherein data is transmitted via wireless transmission 602 between the receiver and the insulin pen 600. In some embodiments, the receiver sends therapy recommendations to the insulin pen, such as amount and time of insulin injection. In some embodiments, the insulin pen sends amount of therapy administered by a patient, such as type, amount, and time of administration. Such information can be used in data analysis, including estimation of analyte values, output of therapy recommendations, and trend analysis, for example.

FIG. 56 is an illustration of a receiver 158 that interfaces with an insulin pump 610, wherein data is transmitted via wireless transmission 612 between the receiver 158 and the insulin pump 610. In some embodiments, the receiver sends therapy recommendations to the insulin pump 610, such as amount and time of insulin administration. In some embodiments, the insulin pump 610 sends information regarding therapy to be administered such as type, amount, and time of administration. Such information can be used in data analysis, including estimation of analyte values, output of therapy recommendations, and trend analysis, for example.

In general, any of the above methods of data input and output can be combined, modified, selectively viewed, selectively applied, or otherwise altered without departing from the scope of the present invention.

Methods and devices that are suitable for use in conjunction with aspects of the preferred embodiments are disclosed in U.S. Pat. Nos. 4,994,167; 4,757,022; 6,001,067; 6,741,877; 6,702,857; 6,558,321; 6,931,327; 6,862,465; 7,074,307; 7,081,195; 7,108,778; 7,110,803; and 7,192,450.

Methods and devices that are suitable for use in conjunction with aspects of the preferred embodiments are disclosed in U.S. Patent Publication No. US-2005-0176136-A1; U.S. Patent Publication No. US-2005-0251083-A1; U.S. Patent Publication No. US-2005-0143635-A1; U.S. Patent Publication No. US-2005-0181012-A1; U.S. Patent Publication No. US-2005-0177036-A1; U.S. Patent Publication No. US-2005-0124873-A1; U.S. Patent Publication No. US-2005-0115832-A1; U.S. Patent Publication No. US-2005-0245799-A1; U.S. Patent Publication No. US-2005-0245795-A1; U.S. Patent Publication No. US-2005-0242479-A1; U.S. Patent Publication No. US-2005-0182451-A1; U.S. Patent Publication No. US-2005-0056552-A1; U.S. Patent Publication No. US-2005-0192557-A1; U.S. Patent Publication No. US-2005-0154271-A1; U.S. Patent Publication No. US-2004-0199059-A1; U.S. Patent Publication No. US-2005-0054909-A1; U.S. Patent Publication No. US-2005-0051427-A1; U.S. Patent Publication No. US-2003-0032874-A1; U.S. Patent Publication No. US-2005-0103625-A1; U.S. Patent Publication No. US-2005-0203360-A1; U.S. Patent Publication No. US-2005-0090607-A1; U.S. Patent Publication No. US-2005-0187720-A1; U.S. Patent Publication No. US-2005-0161346-A1; U.S. Patent Publication No. US-2006-0015020-A1; U.S. Patent Publication No. US-2005-0043598-A1; U.S. Patent Publication No. US-2003-0217966-A1; U.S. Patent Publication No. US-2005-0033132-A1; U.S. Patent Publication No. US-2005-0031689-A1; U.S. Patent Publication No. US-2004-0186362-A1; U.S. Patent Publication No. US-2005-0027463-A1; U.S. Patent Publication No. US-2005-0027181-A1; U.S. Patent Publication No. US-2005-0027180-A1; U.S. Patent Publication No. US-2006-0020187-A1; U.S. Patent Publication No. US-2006-0036142-A1; U.S. Patent Publication No. US-2006-0020192-A1; U.S. Patent Publication No. US-2006-0036143-A1; U.S. Patent Publication No. US-2006-0036140-A1; U.S. Patent Publication No. US-2006-0019327-A1; U.S. Patent Publication No. US-2006-0020186-A1; U.S. Patent Publication No. US-2006-0020189-A1; U.S. Patent Publication No. US-2006-0036139-A1; U.S. Patent Publication No. US-2006-0020191-A1; U.S. Patent Publication No. US-2006-0020188-A1; U.S. Patent Publication No. US-2006-0036141-A1; U.S. Patent Publication No. US-2006-0020190-A1; U.S. Patent Publication No. US-2006-0036145-A1; U.S. Patent Publication No. US-2006-0036144-A1; U.S. Patent Publication No. US-2006-0016700-A1; U.S. Patent Publication No. US-2006-0142651-A1; U.S. Patent Publication No. US-2006-0086624-A1; U.S. Patent Publication No. US-2006-0068208-A1; U.S. Patent Publication No. US-2006-0040402-A1; U.S. Patent Publication No. US-2006-0036142-A1; U.S. Patent Publication No. US-2006-0036141-A1; U.S. Patent Publication No. US-2006-0036143-A1; U.S. Patent Publication No. US-2006-0036140-A1; U.S. Patent Publication No. US-2006-0036139-A1; U.S. Patent Publication No. US-2006-0142651-A1; U.S. Patent Publication No. US-2006-0036145-A1; U.S. Patent Publication No. US-2006-0036144-A1; U.S. Patent Publication No. US-2006-0200022-A1; U.S. Patent Publication No. US-2006-0198864-A1; U.S. Patent Publication No. US-2006-0200019-A1; U.S. Patent Publication No. US-2006-0189856-A1; U.S. Patent Publication No. US-2006-0200020-A1; U.S. Patent Publication No. US-2006-0200970-A1; U.S. Patent Publication No. US-2006-0183984-A1; U.S. Patent Publication No. US-2006-0183985-A1; U.S. Patent Publication No. US-2006-0195029-A1; U.S. Patent Publication No. US-2006-0229512-A1; U.S. Patent Publication No. US-2006-0222566-A1; U.S. Patent Publication No. US-2007-0032706-A1; U.S. Patent Publication No. US-2007-0016381-A1; U.S. Patent Publication No. US-2007-0027370-A1; U.S. Patent Publication No. US-2007-0027384-A1; U.S. Patent Publication No. US-2007-0032717-A1; U.S. Patent Publication No. US-2007-0032718 A1; U.S. Patent Publication No. US-2007-0059196-A1; and U.S. Patent Publication No. US-2007-0066873-A1.

Methods and devices that are suitable for use in conjunction with aspects of the preferred embodiments are disclosed in U.S. application Ser. No. 09/447,227 filed Nov. 22, 1999 and entitled “DEVICE AND METHOD FOR DETERMINING ANALYTE LEVELS”; U.S. application Ser. No. 11/654,135 filed Jan. 17, 2007 and entitled “POROUS MEMBRANES FOR USE WITH IMPLANTABLE DEVICES”; U.S. application Ser. No. 11/675,063 filed Feb. 14, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/543,734 filed Oct. 4, 2006 and entitled “DUAL ELECTRODE SYSTEM FOR A CONTINUOUS ANALYTE SENSOR”; U.S. application Ser. No. 11/654,140 filed Jan. 17, 2007 and entitled “MEMBRANES FOR AN ANALYTE SENSOR”; U.S. application Ser. No. 11/654,327 filed Jan. 17, 2007 and entitled “MEMBRANES FOR AN ANALYTE SENSOR”; U.S. application Ser. No. 11/543,396 filed Oct. 4, 2006 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/543,490 filed Oct. 4, 2006 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/543,404 filed Oct. 4, 2006 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/681,145 filed Mar. 1, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/690,752 filed Mar. 23, 2007 and entitled “TRANSCUTANEOUS ANALYTE SENSOR”; U.S. application Ser. No. 11/691,426 filed Mar. 26, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/691,432 filed Mar. 26, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/691,424 filed Mar. 26, 2007 and entitled “ANALYTE SENSOR”; U.S. application Ser. No. 11/691,466 filed Mar. 26, 2007 and entitled “ANALYTE SENSOR”; and U.S. application Ser. No. 11/692,154 filed Mar. 27, 2007 and entitled “DUAL ELECTRODE SYSTEM FOR A CONTINUOUS ANALYTE SENSOR”.

All references cited herein, including but not limited to published and unpublished applications, patents, and literature references, are incorporated herein by reference in their entirety and are hereby made a part of this specification. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

The term “comprising” as used herein is synonymous with “including,” “containing,” or “characterized by,” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to the present application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

The above description discloses several methods and materials of the present invention. This invention is susceptible to modifications in the methods and materials, as well as alterations in the fabrication methods and equipment. Such modifications will become apparent to those skilled in the art from a consideration of this disclosure or practice of the invention disclosed herein. Consequently, it is not intended that this invention be limited to the specific embodiments disclosed herein, but that it cover all modifications and alternatives coming within the true scope and spirit of the invention. 

What is claimed is:
 1. A system for monitoring glucose concentration in a host, the system comprising: a continuous glucose sensor configured to produce a signal indicative of a real-time glucose concentration in a host; and an electronic system comprising an alarm, wherein the electronic system is operably connected to the sensor, wherein the electronic system further comprises a processor configured to estimate a glucose concentration of the host for a future time by selectively applying one or more algorithms from a plurality of estimation algorithms, and wherein the processor is further configured to trigger the alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.
 2. The system of claim 1, wherein the alarm is at least one of a visual alarm, an audible alarm, and a tactile alarm.
 3. The system of claim 1, wherein the predetermined threshold is user configurable.
 4. The system of claim 1, wherein the future time is at least about 5 minutes in the future.
 5. The system of claim 4, wherein the future time is at least about 10 minutes in the future.
 6. The system of claim 5, wherein the future time is at least about 15 minutes in the future.
 7. The system of claim 6, wherein the future time is at least about 20 minutes in the future.
 8. The system of claim 1, wherein the processor is further configured to calibrate the signal using a conversion function, and wherein one of the plurality of estimation algorithms is based on the conversion function to extrapolate glucose data for the future time.
 9. The system of claim 8, wherein the conversion function is calculated from a linear regression.
 10. The system of claim 8, wherein the conversion function is calculated from a non-linear regression.
 11. The system of claim 8, wherein the conversion function is calculated from reference data obtained from a single point glucose measuring device.
 12. The system of claim 11, wherein the single point glucose measuring device is built into the electronic system.
 13. The system of claim 1, wherein the processor is further configured to filter the signal, and wherein the estimated glucose data is calculated from the filtered signal.
 14. The system of claim 1, wherein the processor is further configured to apply at least one boundary to the estimated glucose data for the future time.
 15. The system of claim 14, wherein the boundary is a physiological boundary.
 16. The system of claim 1, wherein the electronic system further comprises a user interface, wherein the processor is further configured to calibrate the signal, and wherein the processor is further configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.
 17. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of a rate of change of the glucose data.
 18. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to an acceleration of the glucose data.
 19. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of the estimated glucose data derived from the plurality predetermined estimation algorithms.
 20. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to a clinical risk associated with the estimated glucose data.
 21. The system of claim 1, wherein the plurality of estimation algorithms programmed into the electronic system is selected from the group of algorithms consisting of first order polynomial regression, second order polynomial regression, third order polynomial regression, an autoregressive algorithm, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, and time-series forecasting.
 22. The system of claim 1, wherein the processor is further configured to generate real-time or historical estimated glucose data based on the signal and wherein the processor is further configured to estimate a glucose concentration of a host for a future time is configured to use the real-time or historical estimated glucose data in the applied one or more algorithms to estimate the glucose data for the future time period.
 23. A device comprising a processor and a computer readable memory, the computer readable memory comprising code configured to instruct the processor to process data from a continuous glucose measuring device, wherein the code comprises: instructions configured to process a signal received from a continuous glucose measuring device, the signal comprising data indicative of a measured glucose concentration of a host; instructions configured to estimate a future glucose concentration of the host by selectively applying one or more algorithms from a plurality of estimation algorithms; and instructions configured to trigger an alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold.
 24. The device of claim 23, further comprising instructions configured to allow a user to modify the predetermined threshold.
 25. The device of claim 23, wherein the future time is at least about 5 minutes in the future.
 26. The device of claim 25, wherein the future time is at least about 15 minutes in the future.
 27. The device of claim 23, further comprising instructions configured to calibrate the signal using a conversion function, and wherein one of the plurality of estimation algorithms the device is based on the conversion function.
 28. The device of claim 27, wherein the conversion function is calculated from a linear regression.
 29. The device of claim 27, wherein the conversion function is calculated from a non-linear regression.
 30. The device of claim 27, wherein the conversion function is calculated from reference data obtained from a single point glucose measuring device.
 31. The device of claim 30, wherein the single point glucose measuring device is integral with the device.
 32. The device of claim 23, further comprising instructions configured to filter the signal, and wherein the instructions configured to estimate glucose data estimate glucose data from the filtered signal.
 33. The device of claim 23, further comprising instructions configured to apply at least one boundary to the estimated glucose data for the future time.
 34. The device of claim 33, wherein the boundary is a physiological boundary.
 35. The device of claim 23, further comprising instructions configured to calibrate the signal and instructions configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on a user interface.
 36. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of a rate of change of the glucose data.
 37. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to an acceleration of the glucose data.
 38. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of the estimated glucose data derived from the plurality predetermined estimation algorithms.
 39. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to a clinical risk associated with the estimated glucose data.
 40. The device of claim 23, wherein the plurality of estimation algorithms programmed into the electronic device is selected from the group of algorithms consisting of first order polynomial regression, second order polynomial regression, third order polynomial regression, an autoregressive algorithm, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, and time-series forecasting.
 41. The device of claim 23, wherein the code further comprises instructions configured to generate real-time or historical estimated glucose data based on the signal and wherein the instructions configured to estimate a glucose concentration of a host for a future time is configured to use the real-time or historical estimated glucose data in the applied one or more algorithms to estimate the glucose data for the future time period.
 42. A method for monitoring a glucose concentration in a host, the method comprising: generating a signal from a continuous glucose measuring device indicative of a glucose concentration in a host; processing, using electronic circuitry, the signal to estimate a glucose concentration of the host for a future time by selectively applying one or more algorithms from a plurality of estimation algorithms; and alarming the host when the estimated glucose data for the future time is above or below at least one predetermined threshold.
 43. The method of claim 42, wherein alarming comprises providing at least one of a visual signal, an audible signal, and a tactile signal.
 44. The method of claim 42, further comprising calibrating the signal using a conversion function, wherein one of the plurality of estimation algorithms programmed into the electronic circuitry is based on the conversion function.
 45. The method of claim 42, further comprising filtering the signal, wherein processing the signal to estimate glucose data estimates the glucose data from the filtered signal.
 46. The method of claim 42, further comprising applying a boundary to the estimated glucose data for the future time.
 47. The method of claim 42, further comprising calibrating the signal and displaying a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface.
 48. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of a rate of change of the glucose data.
 49. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to an acceleration of the glucose data.
 50. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of the estimated glucose data derived from the plurality predetermined estimation algorithms.
 51. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to a clinical risk associated with the estimated glucose data.
 52. The method of claim 42, wherein the plurality of estimation algorithms programmed into the electronic circuitry is selected from the group of algorithms consisting of first order polynomial regression, second order polynomial regression, third order polynomial regression, an autoregressive algorithm, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, and time-series forecasting.
 53. The method of claim 42, further comprising using electronic circuitry to generate real-time or historical estimated glucose data based on the signal and wherein the processing includes using the real-time or historical estimated glucose data in the applied one or more algorithms to estimate the glucose data for the future time period. 